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1/<strong>2020</strong> www.maintworld.com<br />

maintenance & asset management<br />

Rotating Equipment Services:<br />

A comprehensive,<br />

worry-free package p 8<br />


Your best weapon against poor<br />

reliability is knowledge.<br />

You need knowledge. Your colleagues need knowledge. Techniques,<br />

solutions, strategy and the business case - it is all critical knowledge.<br />

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focus is condition monitoring or the bigger picture of reliability<br />

improvement our websites, live events and worldwide communities<br />

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Educational Videos<br />

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600+<br />

Contributors<br />


Break the Break/Fix Cycle<br />

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Reduce Maintenance and Determine<br />

Probable Causes


The COVID-19 Crisis,<br />

a Maintainer’s View<br />

‘NOUS SOMMES EN GUERRE!’ I can’t state any<br />

better than the French President Emmanuel<br />

Macron, that humanity is currently at war with<br />

COVID-19. This type of lung infection is caused<br />

by the Severe Acute Respiratory Syndrome<br />

CoronaVirus 2 (SARS-CoV-2), which is spreading<br />

around the world after an initial outbreak in<br />

the Chinese Wuhan region in December 2019.<br />

Many governments are taking severe measures,<br />

including travel bans, school closures, lockdowns<br />

requiring people to stay at home, factory shutdowns,<br />

etc. The world economy has been impacted<br />

severely; stock markets colour deep red. I don’t<br />

need to explain to you that this situation is unprecedented. Let’s look at the current<br />

situation through the eyes of a maintenance, reliability and asset manager.<br />

Chronicle of a Problem Foretold<br />

Coronavirus infections have already been lurking for several years. Usually these<br />

viruses cause relatively mild symptoms, such as colds in winter and early spring. As<br />

a matter of fact, 5 to 10 percent of colds are caused by coronaviruses. But we knew<br />

from the 2002 SARS and 2012 MERS outbreaks, that coronaviruses could potentially<br />

become very dangerous. However, even after two major wake-up calls, policy<br />

makers and companies were still not willing to make the investment in preventative<br />

measures. We had plenty of time to develop medicines and vaccines to prevent<br />

today's outbreak, but it just did not happen. Sounds familiar, doesn’t it? How many<br />

times do maintainers need to remind upper management that it is better to prevent<br />

than to repent (= feel regret and remorse)? Also, the asset manager knows all<br />

too well that the risk of doing nothing needs to be incorporated into investment<br />

decisions. Unfortunately, this is often not the case. A small short-term gain usually<br />

wins over over long-term benefit. As a result, we now live in times of repent.<br />

System Overload<br />

The risk of developing a severe COVID-19 lung infection through the Coronavirus<br />

increases with age – for the geeks: it is a failure pattern B. Taking into account that<br />

in Europe 18 percent of the population is aged (compared to only three percent in<br />

Africa), hospitals are hit by an exponentially increasing number of patients.<br />

Just as technicians are firefighting an ever-increasing number of failures<br />

caused by overaged assets, doctors and nurses are overwhelmed by the huge demand<br />

on intensive care. Add to this the fact there is no proper treatment and it<br />

becomes clear we are headed for a complete health system overload.<br />

Valuable Assets Need to be Protected<br />

Just as it is the maintenance technicians that keep things running in a factory, it is<br />

the doctors and nurses (and hospital technicians) who are our most valuable assets<br />

at the front of the war against COVID-19. It goes without saying that, if we want to<br />

win the battle, we need to protect those doctors and nurses from being infected.<br />

Enter the facemasks. The global shortage of facemasks has revealed another major<br />

risk in today’s society. The relentless offshoring of manufacturing has led to very<br />

vulnerable global supply chains. If there is a single lesson to be learned from the current<br />

Corona Crises, then it is the fact that decision takers need to do more and better<br />

long-term thinking. All of us can help by making the right choices ourselves.<br />

Stay healthy!<br />

Wim Vancauwenberghe<br />

Maintenance Evangelist<br />

34<br />

A<br />

good root cause<br />

elimination program<br />

needs a process, practical<br />

training in critical<br />

thinking, and coordination<br />

and follow-up of actions.<br />

6 maintworld 1/<strong>2020</strong>

IN THIS ISSUE 1/<strong>2020</strong><br />

20<br />

There<br />

are many ways Asset<br />

Performance Management<br />

(APM) 4.0 helps you gain<br />

insights from data and<br />

optimize asset performance.<br />

=<br />

46<br />

Chemicals plants often<br />

have plenty of good<br />

data on equipment<br />

performance and reliability.<br />

A predictivemaintenance<br />

program might be the worst<br />

way to use it.<br />

8<br />

Rotating Equipment Services: A<br />

comprehensive, worry-free package<br />

12<br />

16<br />

20<br />

24<br />

Self-Inflicted Reliability Problems of<br />

Rotating Machinery<br />

Cloud-Enabled, On-Premises, or<br />

Both?<br />

DATA IS ABUNDANT Insights and<br />

Actionable Information are Hard to<br />

Find<br />

Experience Feedback – Rotating<br />

Machinery<br />

28<br />

32<br />

34<br />

36<br />

38<br />

RESONANCE - The Hidden Threat<br />

How predictive maintenance<br />

enhances plant safety<br />

Root Cause of an Electrical Problem,<br />

Did You Find the Systematic<br />

Problem to Solve?<br />

The Day After Tomorrow in Asset<br />

Performance<br />

A standardised methodology with<br />

factory specific outcome Multi-site<br />

approach with VDMXL<br />

40<br />

43<br />

46<br />

48<br />

Viewing Maintenance as a System<br />

to Optimize Performance<br />

Use of High-speed Thermography<br />

in Laser High-temperature Capillary<br />

Gap Brazing<br />

Predictive Maintenance: The Wrong<br />

Solution to the Right Problem in<br />

Chemicals<br />

Industrial AI in Maintenance: False<br />

Hopes or Real ACHIEVEMENTS?<br />

Issued by Promaint (Finnish Maintenance Society), Messuaukio 1, 00520 Helsinki, Finland tel. +358 29 007 4570<br />

Publisher Omnipress Oy, Mäkelänkatu 56, 00510 Helsinki, tel. +358 20 6100, toimitus@omnipress.fi, www.omnipress.fi<br />

Editor-in-chief Nina Garlo-Melkas tel. +358 50 36 46 491, nina.garlo@omnipress.fi, Advertisements Kai Portman, Sales<br />

Director, tel. +358 358 44 763 2573, ads@maintworld.com Layout Menu Meedia, www.menuk.ee Subscriptions and<br />

Change of Address members toimisto@kunnossapito.fi, non-members tilaajapalvelu@media.fi Printed by Painotalo Plus<br />

Digital Oy, www.ppd.fi Frequency 4 issues per year, ISSN L 1798-7024, ISSN 1798-7024 (print), ISSN 1799-8670 (online).<br />

1/<strong>2020</strong> maintworld 7


Rotating Equipment Services:<br />

A comprehensive, worry-free package<br />

for pumps, turbines, compressors, etc.<br />

Many processing plants include rotating equipment that is in continual use and<br />

contributes significantly to the enterprise’s productivity. This equipment must be<br />

operated in a cost-effective manner and must not break down. To ensure this,<br />

Bilfinger offers rotating equipment services covering the entire lifecycle of the<br />

systems involved. A project currently underway at the fertilizer producer Yara<br />

Porsgrunn in Norway shows how this works in practice.<br />

BERNARDO SEQUEIRA, Business Development Rotating Equipment, Bilfinger SE;<br />

ØYSTEIN HALDORSEN, Department Manager, Rotating Machinery, Bilfinger Industrial Services Norway AS.<br />

BILFINGER’S NETWORK for the provision<br />

of rotating equipment services covers all<br />

of Europe and consists of several centers<br />

of competence; the intention is to<br />

further expand this network in future.<br />

It comprises a large number of technical<br />

specialists like engineers and technicians<br />

and allows Bilfinger’s national<br />

companies to share their expertise along<br />

with the experiences they have gained<br />

with regional particularities. Customers<br />

benefit from having a service provider on<br />

call who can offer all the required services<br />

from under one roof, across national<br />

boundaries and regardless of the type of<br />

machinery involved. Bilfinger aims to<br />

position itself as a complementary service<br />

provider alongside OEMs (original<br />

equipment manufacturers).<br />

The target industries in this context<br />

are chemical & petrochemical, oil & gas,<br />

as well as energy & utilities. Obviously,<br />

every plant features different types of<br />

equipment. Bilfinger has classified rotating<br />

equipment into two categories and<br />

has set up a center of competence for each<br />

one. The first is “heavy rotating equipment,”<br />

which includes turbomachinery,<br />

piston compressors as well as multistage<br />

high-pressure pumps and gear units. The<br />

second is “small rotating equipment,”<br />

which comprises standard pumps and<br />

drive motors. In all cases, Bilfinger provides<br />

end-to-end support specifically<br />

tailored to its customers’ needs, including<br />

manufacturer-independent consulting,<br />

installation and commissioning of new<br />

equipment; maintenance, repair and ongoing<br />

optimization during operation; as<br />

well as the final decommissioning.<br />

Investment phase, installation<br />

and commissioning<br />

The lifecycle of a piece of machinery<br />

essentially consists of four phases: Its<br />

initial acquisition in the investment<br />

phase, its installation and commissioning,<br />

the operating phase, and the final<br />

decommissioning. Each of these lifecycle<br />

phases is fully covered by Bilfinger’s rotating<br />

equipment services.<br />

8 maintworld 1/<strong>2020</strong>


During the investment phase, Bilfinger<br />

advises its customers as to which<br />

equipment will best suit their needs,<br />

while offering feasibility and engineering<br />

studies along with the planning to be<br />

submitted along with the building permit<br />

application. If so requested, Bilfinger<br />

can also provide the full range of asset<br />

management services for small rotating<br />

equipment. In this case, Bilfinger offers<br />

its customer an equipment-rental pool<br />

from which roughly 20,000 devices such<br />

as pumps, electric motors or frequency<br />

converters, etc. can be leased at reasonable<br />

cost. This ensures the highest possible<br />

level of availability, as do comprehensive<br />

support services for the repair<br />

or replacement of faulty devices – which<br />

can be provided in a matter of hours, depending<br />

on location.<br />

The arrangement offered by Bilfinger<br />

known as the “Value Performance<br />

Contract” is unique on the market. It<br />

offers customers a guarantee for a respective<br />

pump’s availability in return for<br />

Operating phase and<br />

decommissioning<br />

During the operating phase, Bilfinger’s<br />

task is to provide detailed maintenance<br />

services and regular repairs for the customer’s<br />

rotating equipment. Since these<br />

services are performed on a continual<br />

basis, any long or unexpected downtimes<br />

are prevented that potentially could result<br />

in huge losses of production. Upkeep<br />

and repair measures of a more extensive<br />

nature are planned far in advance and<br />

in constant coordination with the customer.<br />

This ensures that any protracted<br />

downtimes can be kept to a minimum.<br />

Likewise, the developments in the<br />

field of industrial automation (“Industry<br />

4.0”) are highly relevant to rotating<br />

equipment services, specifically when it<br />

comes to prescriptive maintenance. For<br />

example, vibration sensors can be used<br />

to monitor the status of rotating machines<br />

and to alert the competent maintenance<br />

team should the monitored parameters<br />

deviate from their target range. This<br />

allows the maintenance team to intervene<br />

promptly and head off any impending<br />

damage. One service Bilfinger Industrial<br />

Services Norway offers its customers is<br />

the evaluation of the vibration signals<br />

emitted by compressors and turbines. On<br />

that basis, irregular running noises are analyzed<br />

and attributed to a specific cause,<br />

so that appropriate action can be taken<br />

immediately if required. This type of status<br />

monitoring protects the machinery<br />

involved from unexpected breakdowns.<br />

In Germany, Bilfinger distributes a vibration<br />

sensor specifically developed for the<br />

monitoring of pumps, and also provides<br />

supervision and evaluation services.<br />

The optimization and modification of<br />

rotating equipment is another field that<br />

Bilfinger looks after. Here, the main objective<br />

is to realize potential energy-savings –<br />

a significant aspect given that energy consumption<br />

accounts for about 80 percent<br />




a fixed price, along with contractually<br />

assured, progressive reductions of the<br />

maintenance costs. Bilfinger delivers the<br />

desired rotating equipment and provides<br />

support services to ensure it continues<br />

to be functional over a longer term.<br />

of the lifecycle costs of many pumps. Up to<br />

60 percent of these costs potentially could<br />

be saved by adjusting the respective pump<br />

to run at its optimal operating point.<br />

Technicians from the Bilfinger network<br />

analyze the relevant systems and draw<br />

on their decades of experience to prepare<br />

and implement proposals for technical<br />

improvement.<br />

A case in practice: Yara<br />

Porsgrunn in Norway<br />

Depending on a customer’s requirements,<br />

each service will be provided by a specific<br />

team drawn from the network. In Norway,<br />

Bilfinger is in a unique position since the<br />

center of competence for heavy rotating<br />

equipment located there constitutes the<br />

largest trove of experience in both onshore<br />

and offshore services. One example<br />

is Yara Porsgrunn, a Norwegian fertilizer<br />

producer running four nitric acid plants, a<br />

key input material for fertilizer. These facilities<br />

are among the biggest of their kind<br />

in the world, with a total output capacity<br />

of 1.8 million tons per annum. Bilfinger’s<br />

task was to recondition a compressor<br />

train during a scheduled downtime, while<br />

concomitantly performing a chemical<br />

10 maintworld 1/<strong>2020</strong>


cleaning and oil-flush of the lubricating/<br />

hydraulic system. The compressor train<br />

consists of a steam turbine, a centrifugal<br />

compressor, a gear unit, an axial compressor<br />

and an expander, all of which<br />

were coupled to a drive shaft.<br />

The biggest challenge in this case was<br />

to carry all out the activities simultaneously<br />

in order to keep the stop time as<br />

brief as possible. The work was performed<br />

by roughly 3 engineers and 50<br />

technicians supplied by Bilfinger, who<br />

worked as a team with Yara Porsgrunn<br />

with added assistance from MAN Energy<br />

Solutions as the OEM. A local Bilfinger<br />

workshop manufactured a number of<br />

replacement parts and was able to perform<br />

repairs on site. This was a significant<br />

advantage, since the delivery times<br />

of the OEM were too long. Thanks to<br />

Bilfinger’s seasoned experts, the project<br />

ran smoothly. Despite the unexpected<br />

discovery of worn-out parts, the scheduled<br />

downtime of three weeks was only<br />

slightly exceeded as a consequence of the<br />

flexible approach taken by Bilfinger and<br />

its technical expertise.<br />

Rotating equipment services in<br />

the future<br />

The end-to-end package offered by<br />

Bilfinger bundles all the required services<br />

under one roof, while leaving them customizable<br />

to each customer’s needs. The<br />

advantage this service delivers over OEMs<br />

is that it allows customers to obtain independent<br />

advice and to tap into expertise<br />

transcending that of any one manufacturer.<br />

Compared to its competitors in the<br />

field of rotating equipment, Bilfinger also<br />

is able to deliver a greater amount of qualified<br />

manpower. Roughly 200 technical<br />

specialists for heavy rotating equipment<br />

currently belong to the network.<br />

In other words, Bilfinger is in an<br />

excellent position to handle upcoming<br />

developments on the market, particularly<br />

in view of the fact that cooperation<br />

models will become increasingly important<br />

in future: Global customers are<br />

looking for providers who are able to<br />

offer services of consistently high quality<br />

across the globe, while ensuring that<br />

these services are steered centrally. The<br />

demand for the rotating equipment services<br />

offered by Bilfinger as a leading international<br />

industrial services provider<br />

is certain to grow steadily in the years<br />

to come – also because customers need<br />

to fill the prevailing shortfall in skilled<br />

personnel with the required expertise<br />

in rotating equipment.<br />

1/<strong>2020</strong> maintworld 11


Self-Inflicted Reliability Problems<br />

of Rotating Machinery<br />

The root cause of poor reliability can come from many sources. You may<br />

experience reliability issues due to the age of your plant. Or perhaps poor design<br />

decisions were made. Or the original construction crew cared nothing for reliability.<br />

And there may be other reasons, outside of your control, that resulted in the<br />

reliability problems you experience today.<br />


initiative, you will need to address<br />

these issues, but first, you need to address<br />

the self-inflicted reliability issues.<br />

“But we don't have self-inflicted reliability<br />

problems."<br />

It is a bitter pill to swallow, but yes,<br />

you do. But that is good news because<br />

it is much easier to deal with the selfinflicted<br />

root causes than the inherent<br />

reliability problems you adopted.<br />

What are self-inflicted<br />

reliability problems?<br />

In order to determine why equipment<br />

fails prematurely (or why you experience<br />

slowdowns, safety incidences, or quality<br />

problems), you could go through a<br />

detailed failure modes, effects, and criticality<br />

(FMECA) analysis process, or you<br />

could perform root cause failure analysis<br />

12 maintworld 1/<strong>2020</strong><br />


ARP-E/L, CMRP<br />

Mobius Institute<br />

(RCFA) after each failure occurs. Or you<br />

could learn from the experience gained<br />

at thousands of plants around the world<br />

and consider some of the most common<br />

root causes of equipment failure - we will<br />

focus on rotating machinery.<br />

The number three cause of<br />

reliability problems<br />

Let’s start with the most obvious problems<br />

and then we will work backward to<br />

their root causes.<br />

Most equipment, like motors, pumps,<br />

fans, compressors, and turbines, are<br />

designed to run for many, many years<br />

without unplanned downtime. Yes, they<br />

may have some components that wear<br />

out, but many of the components, such<br />

as the bearings and gears, are designed to<br />

give years of trouble-free operation. But<br />

that assumes that all of the parts were<br />

installed correctly, the components are<br />

precision-aligned, the bearings and gears<br />

are correctly lubricated, all fasteners are<br />

tightened to the correct tension, there<br />

is no resonance, belts are tightened to<br />

the correct tension, and the rotors are<br />

precision-balanced.<br />

And it assumes that the equipment<br />

is operated as per design. Pumps, for<br />

example, should be operated at their<br />

“best efficiency point.”<br />

What happens at your plant? Do these

The<br />

The<br />

The Uptimization<br />

Uptimization Experts.<br />

Experts.<br />

What does<br />


mean to you?<br />

marshallinstitute.com<br />



root causes exist? If you are not sure,<br />

then they almost certainly do.<br />

We will now take a quick look at just<br />

a few of those areas so that you can see<br />

why seemingly minor issues cause such<br />

serious problems.<br />

Shaft alignment<br />

When two shafts are “collinear” (no<br />

angle or offset between their centerlines),<br />

it reduces the stress on the bearings,<br />

couplings, shafts, and the rest of<br />

the machine components. Research was<br />

performed that revealed that just 5/60 th<br />

of a degree of angular misalignment can<br />

halve the life of your bearings.<br />

If you use laser alignment with<br />

appropriate tolerances, and you remove<br />

soft foot in all its forms (base issues, pipestrain,<br />

etc.), then you will have eliminated<br />

a common root cause of failure.<br />

Balancing<br />

When you balance to ISO 21940-11 grade<br />

G 1.0, the cyclical forces on the bearings,<br />

shaft, and structure are minimized, and<br />

thus you gain greater reliability. If you<br />

do not have a balancing standard, then<br />

unbalance will be a root cause of failure.<br />

And if you wait until the unbalance<br />

generates “high” vibration, “forcing”<br />

you to perform corrective maintenance,<br />

then you will have reduced the life of the<br />

equipment and supporting structure.<br />

Why is that? The life of a bearing is<br />

inversely proportional to the cube of the<br />

load. That sounds very complicated, but<br />

an easier way to say it is that if you double<br />

the load, the life will be reduced to<br />

one-eighth (23).<br />

Therefore, while the rotor is out-ofbalance,<br />

the bearings are being stressed,<br />

and their life expectancy will be reduced.<br />

Misalignment also generates these forces,<br />

and that is why it must be minimized.<br />

The unbalance is also generating<br />

forces that stress the structure, potentially<br />

resulting in fatigue failure of the<br />

structure itself or its foundations.<br />

The unbalance forces are also amplified<br />

by resonance. The structure will<br />

“naturally” vibrate back-and-forth, or<br />

side-to-side, or in other ways at certain<br />

frequencies. If the vibration generated<br />

by unbalance (or misalignment, or<br />

pump-vane vibration, or other avoidable<br />

“forcing frequencies”) is close to one of<br />

these natural resonant frequencies, the<br />

motion will be amplified. That is not<br />

good for the machine or structure.<br />

Lubrication<br />

When you correctly lubricate bearings<br />

and gears, whether you use grease or<br />

oil, and that lubricant is free of contaminants,<br />

you will achieve maximum<br />

life. But if bearings are not adequately<br />

greased, their life will be reduced. If the<br />

oil is contaminated, or the viscosity is<br />

incorrect, or the additives are depleted,<br />

then the life of gears and bearings will be<br />

greatly reduced.<br />

Research was performed to determine<br />

which particles caused the greatest<br />

damage. It was not the 40 µm particles,<br />

or the 10 µm particles - it was the tiny<br />

“3-5 µm” particles.<br />

And you may think that if you can’t<br />

see the water in oil, then the oil must<br />

be fine. Sadly, that is not correct. By the<br />

time you can see the water, the life of the<br />

bearing has been reduced by 70 percent.<br />

We could continue the discussion,<br />

but suffice to say that there is a great deal<br />

we can do to avoid problems that arise<br />

due to imperfect maintenance and operating<br />

practices.<br />

The number two cause of<br />

reliability problems<br />

It is one thing to understand all of the<br />

root causes we have just discussed – and<br />

there are many others – but it is another<br />

thing to be able to get approval to establish<br />

standards and purchase all of the<br />

tools, such as laser alignment systems,<br />

that enable the technicians and operators<br />

to do the job correctly. But owning<br />

the tools and having standard operating<br />

procedures will not solve the problem.<br />

The problem will only be solved when<br />

the maintenance technicians and operators<br />

want to use them properly, and they<br />

are given the time and encouragement to<br />

use them.<br />

So we will need to address the desire,<br />

i.e., the culture. Culture is the key to<br />

success.<br />

The number one cause of<br />

reliability problems<br />

A strong case could be made that the root<br />

cause of all failures ultimately derives<br />

from the lack of senior management support<br />

for a culture that values reliability.<br />

Without their support, it will be impossible<br />

to change the culture and thus<br />

change behaviour.<br />

Just think of the initiative to improve<br />

safety at your plant. If senior management<br />

did not support it, do you think<br />

your plant would have made the gains<br />

14 maintworld 1/<strong>2020</strong>


that it has made? Senior management<br />

enabled people to be employed in safety<br />

roles, it invested in the training and tools,<br />

it agreed to signage that provided warning<br />

and feedback on progress, it stood<br />

strong when there were opportunities to<br />

cut corners that would risk safety, and it<br />

made it quite clear how important safety<br />

is to the future of the organization. (Well,<br />

I hope that is the case at your plant.)<br />

You need the same thing to happen<br />

with reliability improvement.<br />

Everyone within the organization<br />

needs to understand that reliability is<br />

critically important to the organization<br />

and that senior management will stand<br />

strong when shortcuts that compromise<br />

reliability are proposed.<br />

Therefore, you need to gain senior<br />

management support so you can change<br />

the culture and thus successfully implement<br />

a reliability improvement initiative<br />

that eliminates the self-inflicted root<br />

causes.<br />

But wait, is there more?<br />

Since we are discussing root causes, let’s<br />

consider the root cause of the lack of senior<br />

management support. Is it their fault<br />

for not appreciating the opportunity<br />

to improve reliability, or is it your fault<br />

because you have not presented the business<br />

case for reliability improvement?<br />

It is common for people to talk about<br />

the “commonsense” benefits of reliability<br />

improvement. It is also common<br />

for reliability and condition monitoring<br />

teams to assume that senior management<br />

appreciates the benefits of what<br />

they have achieved, without ever<br />

communicating the financial benefits<br />

of their actions.<br />

Therefore, perhaps the true root<br />

cause of poor reliability is the inability<br />

(or unwillingness) of reliability and<br />

condition monitoring team leaders to<br />

establish a business case, sell the business<br />

case, and continually communicate<br />

the value of the reliability improvement<br />

initiative.<br />

Where does condition<br />

monitoring fit into this?<br />

Many people will believe that if they<br />

have a condition monitoring program,<br />

the reliability will be optimized. Sadly,<br />

that is not true. Most faults detected are<br />

avoidable, and while it is important to<br />

get an early warning, it is much more important<br />

to avoid the problem in the first<br />

place. Condition monitoring can help by<br />

detecting the root causes of failure: misalignment,<br />

unbalance, lubrication issues,<br />

looseness, and so on. If those problems are<br />

cost-effectively nipped in the bud, then we<br />

will avoid future failures.<br />

Another way that condition monitoring<br />

can play an important role is by performing<br />

acceptance testing. As part of the<br />

purchase agreement, the condition monitoring<br />

specialists can perform tests to ensure<br />

the new or overhauled equipment is<br />

“defect-free.” You may be surprised at how<br />

many problems you bring into the plant.<br />

Learning more<br />

As you can imagine, there is a great<br />

deal more that could be said about all<br />

of these topics. In an attempt to clarify<br />

the process we are discussing in this<br />

article, we developed a process called<br />

Asset Reliability Transformation, or ART.<br />

You can learn more, without charge, at<br />

www.reliabilityconnect.com.<br />

Conclusion<br />

The condition monitoring group has an<br />

important role to play. Providing an<br />

early warning minimizes the impact of<br />

premature failure, and detecting and<br />

eliminating the root causes ensures that<br />

we achieve the greatest life and value<br />

from our precious assets.<br />

The reliability improvement team<br />

has an even more important role to play.<br />

Proactively eliminating the root causes<br />

of failure ensures there will be fewer<br />

failures.<br />

But trying to improve reliability<br />

without aligning every activity to the<br />

goals of the organization, and thus<br />

gaining support from senior management,<br />

which then drives the necessary cultural<br />

changes, will never achieve the true<br />

potential of the initiative.<br />

Asset Reliabiity<br />

Transformation.<br />

The Key to a<br />

Happy Life.<br />

1/<strong>2020</strong> maintworld 15


Cloud-Enabled,<br />

On-Premises,<br />

or Both?<br />

Which Data Structure<br />

Works Best for<br />

Your Maintenance<br />

Operations?<br />

ICONICS develops automation software that visualizes, archives, analyzes, mobilizes,<br />

and cloud-enables organizations’ data. With the expansion of the Industrial Internet<br />

of Things (IIoT), the data processes involved in many automation-based capabilities<br />

can be performed either on-premises (as has traditionally been done by the<br />

majority of organizations worldwide) or via the cloud (which has increasingly and<br />

rapidly become a more attractive option).<br />

THERE ARE MULTIPLE reasons why organizations’<br />

maintenance operations<br />

would select IIoT-integrated monitoring<br />

and control solutions. Among these are:<br />

• Reduced on-site hardware obsolescence<br />

• •Secure access across multiple<br />

locations<br />

• Expanded connectivity<br />

Reduced On-site Hardware<br />

Obsolescence<br />

A business starts or an organization<br />

forms. It begins to amass the equipment<br />

needed to perform its functions, as<br />

16 maintworld 1/<strong>2020</strong><br />


IoT Business<br />

Development Manager,<br />


well as the automation solutions it will<br />

deploy. Oftentimes, these same entities<br />

also begin their own maintenance operations<br />

in order to keep their hardware in<br />

good working condition. Just as a company/organization<br />

accumulates equipment<br />

to run the business itself, it also<br />

accumulates IT hardware (servers, PCs,<br />

networking equipment, etc.) that is used<br />

to perform related tasks (such as those<br />

critical to automation and maintenance<br />

operations).<br />

However, just like business-focused<br />

machinery (e.g., an aging piece of manufacturing<br />

equipment), an organization’s<br />

IT equipment can also start showing<br />

signs of aging. What may have been sufficient<br />

even just a few years ago may no<br />

longer be comparable to newer machines<br />

that can perform more advanced tasks.<br />

Organizations are now able to consider<br />

the costs associated with upgrading/


retrofitting/replacing their onsite IT<br />

machinery versus the cost of moving applications<br />

to the cloud, where the cloud<br />

service operators are responsible for<br />

agreed-upon performance of their equipment.<br />

Many customers of cloud service<br />

providers are able to take advantage of<br />

an increase in processor performance to<br />

be able to take care of the heavy lifting of<br />

such tasks as advanced data analytics or,<br />

specifically for maintenance operations,<br />

predictive maintenance / fault detection<br />

and diagnostics (FDD) applications.<br />

Cloud service customers also appreciate<br />

the increase in capacity for rapid big data<br />

storage and retrieval.<br />

There are even ways for existing production<br />

equipment to take advantage of<br />

the cloud. Even though newer modern<br />

equipment may come with the ability<br />

to tie in directly to the IIoT, some older<br />

hardware can be used with emerging<br />

edge devices (IoT gateways) to make it<br />

easier and more cost-effective to become<br />

“cloud-enabled” instead of undergoing<br />

a full replacement. These gateways<br />

provide multiple other benefits, as well,<br />

including the integration of multiple<br />

devices, sensors, and other equipment to<br />

publish messages to the cloud independently<br />

from subscribers. Software modules<br />

built into such gateways decrease latency,<br />

provide edge data processing, and<br />





empower edge analytics with onboard<br />

FDD and workflow technologies with<br />

real-time visualization of KPI data.<br />

Secure Access Across<br />

Multiple Locations<br />

Cloud security is accomplished by<br />

methods between both the applications<br />

themselves and the cloud service<br />

being used. For instance, this could be<br />

between the applications being accessed<br />

through an on-site edge device/IoT<br />

gateway and Microsoft Azure, to name<br />

one of many cloud services. Software,<br />

such as ICONICS IoTWorX, running<br />

on the IoT gateway can be provisioned<br />

and can communicate data securely via<br />

the Microsoft Azure IoT Hub, taking advantage<br />

of the inherent security features<br />

that come with an Azure subscription.<br />

Additionally, cloud-based automation<br />

1/<strong>2020</strong> maintworld 17


(HMI/SCADA, historian, analytics, etc.)<br />

systems are backed up securely off site<br />

from where the data originates, keeping<br />

it safe from equipment damage or natural<br />

disasters.<br />

Such decentralized security measures<br />

are compelling for businesses/organizations<br />

that outgrow one central geographical<br />

location. Global enterprises,<br />

especially, see the benefits in trusting the<br />

security options provided through their<br />

cloud service providers and the integrated<br />

software that utilizes them. ICONICS<br />

IoTWorX, for instance, can connect multiple<br />

buildings, factories, and equipment<br />

through secure TLS encryption and popular<br />

cloud platforms, such as Microsoft<br />

Azure and Amazon Web Services. Data<br />

can be accessed from anywhere through<br />

a pub/sub architecture for real-time<br />

visualization of KPI data at the edge.<br />

IoTWorX delivers an efficient, secure<br />

connection to the cloud through bidirectional<br />

AMQP for Microsoft Azure,<br />

as well as MQTT, REST, and WebSockets<br />

for third-party cloud providers.<br />

Expanded Connectivity<br />

Another benefit of utilizing cloud-based<br />

automation solutions is that there is often<br />

an increase in the number of available<br />

communication protocols that can be<br />

used. This is in addition to the advanced<br />

security measures (bidirectional AMQP<br />

transport protocol [for Microsoft Azure]<br />

and MQTT, REST, and WebSockets [for<br />

third-party providers]) that cloud-based<br />

solutions provide.<br />

For maintenance operations, it’s definitely<br />

a benefit to be able to “talk to” as<br />

many of the machines within the organization<br />

as possible. As an example, ICON-<br />

ICS IoTWorX software is compatible<br />

with multiple standard communication<br />

protocols. These include protocols specific<br />

to plant floor applications; such as<br />

OPC Classic, OPC Unified Architecture<br />

(OPC UA), and Modbus; as well as those<br />

specific to building automation (BACnet)<br />

and IT hardware (SNMP). This provides<br />

users with the ability to communicate<br />

with a wider array of connected<br />

equipment, ultimately enabling users to<br />

better detect potential issues and utilize<br />

an organization’s data, wherever it might<br />

be created, transmitted, or stored.<br />

Cloud Contingencies<br />

For those concerned about the viability<br />

of cloud-based solutions during interruptions<br />

to internet service, there are<br />

measures that can be put into place to<br />

help ensure data doesn’t go missing or<br />

get corrupted. ICONICS has solutions<br />

that provide rapid data archiving and retrieval,<br />

including a "store-and-forward"<br />

feature that is useful when a network<br />

connection is unavailable; one specifically<br />

edge-based (IoT Hyper Collector)<br />

and the other traditionally on-premises<br />

or cloud-enabled (Hyper Historian).<br />

IoT Hyper Collector is part of IoT-<br />

WorX, the previously mentioned micro-<br />

SCADA software suite installed on a<br />

third-party IoT edge device. The collector<br />

has the ability to replay buffered<br />

data back locally, as well as to store and<br />

forward to the cloud when connectivity<br />

is present. For a more traditional<br />

on-premises approach, ICONICS Hyper<br />

Historian Collector also utilizes a similar<br />

store-and-forward feature. If a collector<br />

has lost connectivity to the logger, it will<br />

continue to buffer the data until connectivity<br />

is reestablished.<br />

Best of Both<br />

While the IoT Hyper Collector and<br />

Hyper Historian Collector are examples<br />

of how to retain data integrity both via<br />

the cloud and on-premises, respectively,<br />

there is nothing to prevent an organization<br />

from taking a hybrid approach.<br />

This bridges the gap between OT and<br />

IT and alleviates any "silo effect" of the<br />

organization’s data collection, storage,<br />

and retrieval. The same can be said for an<br />

organization’s entire automation solution<br />

and related data, as a whole. Some<br />

may benefit from a strictly cloud-based<br />

solution. Others may still have reason<br />

to remain with an entirely on-premises<br />

one. However, neither has any restriction<br />

towards using elements of the other<br />

in such a hybrid scenario.<br />

Each organization will make its own<br />

determination regarding what works<br />

best for their business processes and operations<br />

(cloud, on-premises, or hybrid),<br />

as well as to the automation software<br />

vendors that can best support it.<br />

18 maintworld 1/<strong>2020</strong>




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E: info@uesystems.eu<br />

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(webinars, articles, tutorials)<br />





Insights and Actionable<br />

Information are Hard to Find<br />

Plant Managers Can<br />

Improve Decision Making<br />

with Asset Performance<br />

Management 4.0 and<br />

Digital Twins<br />

SAVVY PLANT EXECUTIVES have noticeably<br />

changed how they approach topline<br />

revenue growth opportunities and<br />

bottom-line cost reductions. In assetintensive<br />

industries, digital advancement<br />

strategies like Asset Performance<br />

Management 4.0 and digital twins are<br />

becoming critical strategies for operational<br />

excellence.<br />

Within a plant, digital twins empower<br />

engineering, operations, and maintenance<br />

to collaborate and capitalize<br />

on the opportunity of Industry 4.0. If<br />

common pitfalls can be overcome, such<br />

as data overload, the lack of systems<br />

integration and interoperability, inconsistent<br />

business processes, and siloed<br />

data, then this open and connected data<br />

environment can become a sustainable<br />

competitive advantage.<br />

Maximizing the<br />

Value of Assets<br />

The reason plant staff collect, store, and<br />

analyze plant data is to improve decision-making<br />

and operations. Ultimately,<br />

they want to maximize the return on<br />

assets, minimize production, labor, and<br />

material costs, reduce the total cost of<br />

asset ownership and investment, and deliver<br />

predictable performance, on time.<br />

We can improve the performance of<br />

the physical assets used for production<br />

through the way people operate equipment,<br />

through improved maintenance<br />

20 maintworld 1/<strong>2020</strong><br />


Sandra DiMatteo is<br />

the Global Director<br />

of Marketing<br />

for Digital Twin<br />

Solutions, Asset and Network<br />

Performance at Bentley Systems.<br />

She has more than 20 years<br />

of experience in digitalization<br />

solutions in asset performance<br />

management and reliability<br />

software, asset lifecycle information<br />

management and EAM enterprise<br />

asset management operating in<br />

a connected data environment in<br />

energy and process industries,<br />

utilities and public infrastructure. She<br />

is an advocate and speaker on digital<br />

twins, IIoT analytics, AI and machine<br />

learning, BIM and asset management<br />

technology solutions. Sandra is on<br />

the Reliability Leadership Institute<br />

Board of Advisors and founded<br />

the Ontario Chapter of the Society<br />

of Maintenance and Reliability<br />

Professionals.<br />

practices, or through design and engineering.<br />

This is not a new concept, as an<br />

average plant might have 30 to 50 design<br />

optimizations, modifications, or additions<br />

per year.<br />

However, the siloed approach separating<br />

engineering, operations, and<br />

maintenance is no longer good enough.<br />

Success now requires a new way forward<br />

with a collaborative focus on asset health<br />

to attain performance targets. A reliability<br />

strategy actively resolves asset health<br />

shortcomings, leading to eliminating<br />

performance shortfalls.<br />

Asset Performance<br />

Management<br />

Asset Performance Management (APM)<br />

focuses on meeting performance requirements<br />

through reliability and takes<br />

a cross-discipline approach to fulfil plant<br />

goals and customer needs. Asset performance<br />

management is the plant manager’s<br />

opportunity to extend asset life<br />

safely and reliably, which avoids capital<br />

expenditures.<br />

Some of the key characteristics of an<br />

Asset Performance Management system<br />

include:<br />

• Asset strategy and risk analysis<br />

• Condition-based or reliability<br />

centered maintenance processes<br />

and practices<br />

• A mobile inspection platform with<br />

augmented reality and virtual reality<br />

capabilities<br />

• Predictive analyses including<br />

statistical modeling, neural networks,<br />

artificial intelligence, and<br />

machine learning<br />

• Spare parts optimization<br />

• Asset lifecycle information management<br />

•<br />

The Tipping Point for IIoT<br />

APM 4.0 is effectively deployed within<br />

a digital twin because the Industrial Internet<br />

of Things (IIoT) movement has<br />

arrived at the tipping point. The cost of<br />

sensors, data connection, and data storage<br />

is now a fraction of what it used to be.<br />

As a result, the amount of raw data being<br />

generated in plants from IIoT sources is<br />

growing exponentially, and many organizations<br />

cannot keep up. Every sensor you<br />

add produces thousands of additional<br />

data points. As a result, making sense<br />

of the data to gain meaningful insights


and get to the right decisions can be<br />

time-consuming and difficult if you are<br />

not sure of its relevance or accuracy.<br />

For most plant managers, the vision of<br />

a completely autonomous plant is still a<br />

pipe dream.<br />

Industry 4.0 can connect physical assets<br />

in the plant to their digital counterparts<br />

to improve the automation of plant<br />

operations and maintenance. Using edge<br />

computing to implement artificial intelligence<br />

and automated rules is a fast and<br />

easy way to alert personnel of problems<br />

that must be addressed. However, edge<br />

computing might not monitor all aspects<br />

of every asset over the long-term. To fully<br />

oversee a facility, you need a systematic,<br />

sustainable approach for tracking<br />

asset health over time with visible, accessible,<br />

and trusted engineering data.<br />

Plant managers have embraced<br />

IIoT out of a desire to eliminate human<br />

senses from inspections, such as seeing a<br />

leak or hearing a malfunctioning motor.<br />

Even with the explosion of sensors that<br />

can detect changes in operating conditions,<br />

the ugly truth is that plants remain<br />

highly people dependent. To improve<br />

automation, plants need an efficient,<br />

effective, and comprehensive program<br />

that goes beyond what people can notice<br />

themselves. Such a program should include<br />

a playbook of fully defined organizational<br />

and business processes, proactive<br />

and predictive asset management<br />

practices, and the right technology that<br />

enables the implementation and execution<br />

of real-time asset performance.<br />

Gain Insights to Make<br />

Effective Decisions<br />

There are many ways APM 4.0 helps you<br />

gain insights from data and optimize<br />

asset performance. Automated rules,<br />

calculations, artificial intelligence, and<br />

machine learning are all valuable methods<br />

to enable faster and more effective<br />

decisions. But, engineering information<br />

for each must be complete, accurate, and<br />

available to ensure you are making the<br />

right decision at the right time. Otherwise,<br />

it becomes harder to mitigate costs<br />

and downtime when the asset fails.<br />

In short, effective decision-making<br />

depends on always knowing the current<br />

state of the asset and becoming informed<br />

immediately when that state changes.<br />

This knowledge should include essential<br />

engineering information, as well as how<br />

to bring the asset back to the as-built,<br />

as-commissioned, or as-designed states.<br />

Asset lifecycle information management<br />

is the backbone of APM 4.0. Components,<br />

structures, systems, and operating<br />

states all change over time due to<br />

wear and tear, operator decisions, and<br />

overall plant conditions. Changes in any<br />

single asset can negatively impact wider<br />

systems and processes. Trustworthy<br />

engineering data enables plant engineers<br />

to determine why a change occurred and<br />

who caused the change.<br />

However, raw sensor data alone might<br />

not be useful as the complexity and<br />

interconnections of piping and process<br />

equipment, systems, instrumentation,<br />

and control devices has increased. Operations<br />

technology relies on analytics<br />

visibility as well as subject matter experts<br />

that can act based on the massive<br />

amount of data being generated. Digital<br />

twins, together with asset performance<br />

management 4.0, can harness that raw<br />

data and create a trusted system of<br />

systems. They can connect data with<br />

processes and identify, consolidate, and<br />

analyze all relevant sources of data to<br />

make asset health more visible and drive<br />

informed decisions and measurable<br />

business results.<br />

Digital Twins Enable<br />

Collaboration between<br />

Engineering, Operations and<br />

Maintenance<br />

A digital twin is a digital representation<br />

of a physical asset, process or system, as<br />

well as the engineering information to<br />

understand and model its performance.<br />

Typically, a digital twin can be continuously<br />

synchronized from multiple federated<br />

sources, including sensors and<br />

continuous surveying, to represent its<br />

near real-time status, working condition<br />

or position. Digital twins enable users to<br />

visualize the asset, check status, perform<br />

analysis and generate insights in order to<br />

predict and optimize asset performance.<br />

1/<strong>2020</strong> maintworld 21


As a result, digital twins eliminate silos<br />

of data to deliver situational awareness<br />

and intelligence.<br />

Additionally, a continually updated<br />

digital twin provides proof of accurate<br />

information needed for regulatory compliance.<br />

Engineering, operations, and<br />

maintenance greatly benefit from the<br />

combination of APM and digital twins.<br />

APM provides the strategy and analytics,<br />

while digital twins unify the data, provide<br />

situational awareness and insights,<br />

and deliver actionable information in the<br />

hands of those who need it when they<br />

need it.<br />

Don’t Forget the ET in Your<br />

IT - OT Strategy<br />

Engineering data is always evolving.<br />

As assets are designed, commissioned,<br />

and operated, new information becomes<br />

generated. Individual assets<br />

also evolve through maintenance and<br />

modifications during the operational<br />

phase. The convergence of information<br />

technology, operations technology, and<br />

engineering technology (or IT-OT-ET)<br />

feeds the digital engineering model and<br />

creates a comprehensive digital twin of<br />

the working assets in the plant, facility,<br />

or network. In addition to communicating<br />

the current state of the asset, the<br />

digital twin can perform operational<br />

and engineering simulations to model<br />

the performance of an asset over time<br />

and evaluate options to improve performance.<br />

Essentially, the digital twin connects<br />

the data from IT-OT-ET in a single portal<br />

view, allowing the team to validate,<br />

visualize, and analyze all plant data in<br />

any format and any data storage location.<br />

A digital twin environment that is<br />

open, interoperable, connected, and contextualized<br />

enables true collaboration<br />

between engineering, operations and<br />

maintenance.<br />

A Day in the Life… Who Needs<br />

a Digital Twin and Why?<br />

From the plant floor to the boardroom,<br />

digital twins quickly give plant staff the<br />

information they need to make important<br />

decisions.<br />


Executives overseeing a division, region,<br />

or the entire global operation need to<br />

track and compare plants and fleets. This<br />

includes identifying high-performing<br />

and under-performing plants, then determining<br />

what makes them succeed or<br />

fail. Executives must also provide stakeholders<br />

with proof that the plants are in<br />

control and that assets are safe and reliable.<br />

Using digital twins enables them to<br />

generate informative visualizations of<br />

large-scale assets and grant a regional or<br />

companywide perspective.<br />

PLANT MANAGERS – Plant managers<br />

must ensure plant production is predictable,<br />

safe, and efficient. They need<br />

continual access to key performance<br />

indicators to identify and evaluate units<br />

that are underperforming. When issues<br />

are reported, managers rely on accurate<br />

information for audits and course-corrections.<br />

By providing a unified digital<br />

twin that provides a complete, consistent<br />

view of plant data, managers allow engi-<br />

22 maintworld 1/<strong>2020</strong>


neering, maintenance, and operations<br />

to collaborate and solve problems more<br />

effectively and efficiently.<br />

ENGINEERS – Engineers must quickly<br />

identify potential operational problems<br />

and consider solutions. They need to determine<br />

what has changed in engineering<br />

models, piping and instrumentation<br />

documents, drawings, or the maintenance<br />

and reliability program. Most<br />

importantly, they need to know that<br />

the data is trustworthy so that they can<br />

investigate, troubleshoot, and make fast<br />

and informed decisions. Digital twins<br />

can provide this peace of mind, especially<br />

when engineers can update digital<br />

twins as needed and view relevant IIoT<br />

and engineering information in an open,<br />

connected data environment.<br />

OPERATIONS – Operators need access<br />

to performance and maintenance data<br />

without having to waste time determining<br />

which data is relevant and which is<br />

not. They must review as-operated data<br />

and historical data alike to understand<br />

what field changes and engineering decisions<br />

were made and why. Digital twins<br />

help operators see the big picture and<br />

optimize production. By supplementing<br />

control systems with performance dashboards<br />

containing real-time performance<br />

data, operators receive a holistic overview<br />

of the complete facility including areas or<br />

units not available in the local DCS.<br />


managers must review upcoming<br />

work before attending production<br />

scheduling meetings. Maintenance, reliability,<br />

and integrity engineers need to<br />

monitor and manage equipment health<br />

as well as piping and vessel integrity to<br />

easily spot trends and bad actors. They<br />

need to know what engineering changes<br />

were made in the past and use rules,<br />

calculations, AI, and machine learning<br />

to analyze data. Digital twins combine<br />

analysis methods and provide valuable<br />

insights to ensure safety, reliability, and<br />

asset integrity.<br />

Digital Transformation<br />

For any successful team, operational<br />

excellence depends on a clear strategy,<br />

skilled players trained on the best techniques,<br />

efficient and effective equipment,<br />

and leadership that can coach the<br />

team to cohesively execute the game<br />

plan. When done correctly, this leads to<br />

consistent wins.<br />

APM 4.0 and digital twins are digitally<br />

transforming plants to help them stay<br />

ahead of the competition. Everyone from<br />

the plant floor to the boardroom needs<br />

insights to make more informed decisions.<br />

Digital twins provide a federated<br />

portal view of all necessary systems and<br />

data, which gives workers at all levels the<br />

insight needed for overall success.<br />


OMAN GAS COMPANY implemented<br />

a digitalized, automated framework<br />

for its reliability and integrity program.<br />

Establishing a connected data<br />

environment and digital workflows<br />

reduced failures and improved reliability<br />

performance by 9%. The technology<br />

transformed the team and<br />

how they manage assets, resulting in<br />

significant economic gains.<br />

ARCELORMITTAL USA successfully<br />

implemented an equipment reliability<br />

initiative, saving USD 2.1 million during<br />

a year-long pilot program at one<br />

of its hot strip mills. The success of<br />

the pilot led to a wider implementation<br />

across 10 key focus areas and a<br />

savings of more than USD 14 million<br />

nationwide over two years. Arcelor-<br />

Mittal adopted asset performance<br />

management best practices, processes,<br />

methodologies, and digitalization<br />

technology to efficiently share<br />

trusted information and change the<br />

company’s maintenance culture from<br />

reactive to proactive.<br />

EPCOR UTILITIES INC. implemented<br />

an ISO 55000-aligned; risk-based<br />

asset management process supported<br />

by Bentley System’s APM technology.<br />

Using asset health indexing, they<br />

gained an understanding of the consequence<br />

of asset failures, including<br />

replacement costs, damage to adjacent<br />

assets, impacts to safety, and<br />

environmental cleanup costs. Coupling<br />

this information with outage<br />

times and electrical load data, they<br />

could better predict the annual risk<br />

cost. The result was lowering their<br />

SAIDI Interruptions Duration Index<br />

score to 0.833, well below the regulated<br />

threshold of 1.15 hours/customer.<br />

BP created a central information<br />

store (CIS) to manage information<br />

needed for operations, including all<br />

documents, tags, metadata, and 3D<br />

model visualization. Using a Microsoft<br />

Azure-based cloud deployment<br />

of Bentley Systems’ AssetWise asset<br />

performance software, the project<br />

team seamlessly integrated engineering<br />

information into operations.<br />

Doing so supports safe, reliable, and<br />

efficient operations throughout the<br />

life of their assets. With safety at the<br />

heart of all operations, BP ensures it<br />

continuously maintains the integrity<br />

of operational information.<br />

1/<strong>2020</strong> maintworld 23


Experience Feedback –<br />

Rotating Machinery<br />

PATRICE DANNEPOND, SDT Ultrasound Solutions<br />

Display of the database in the UAS software<br />

Experience feedback on<br />

the implementation of an<br />

ultrasound-based preventive<br />

maintenance program.<br />

Introduction<br />

In this article, we would like to share an<br />

experience feedback from a paper mill that<br />

has implemented a preventive maintenance<br />

program to monitor rotating machines using<br />

ultrasound technology. SDT International<br />

helped implement this monitoring program<br />

by training and coaching the teams in charge<br />

of mechanical maintenance reliability. The<br />

purpose of this program is to monitor a fleet<br />

of about 70 rotating machines during the year<br />

after its implementation and to extend it to<br />

100 machines over the second year.<br />

Issue<br />

This paper mill has relied on preventive<br />

maintenance for many years, using known<br />

and proven technologies for the monitoring<br />

of rotating machines. In 2018, they decided to<br />

extend this monitoring to equipment with rotation<br />

speeds up to 30 RPM, as well as to their<br />

speed-reducing gears.<br />

They purchased an SDT270 type ultrasound<br />

detector, in DU version, along with its<br />

UltrAnalysis (UAS) software, and SDT International<br />

and the Reliability department of<br />

the paper mill developed a training program<br />

suited to the rotating machinery monitoring<br />

program. The first step consisted in creating<br />

the database including these 70 machines,<br />

and then in recording an initial measurement<br />

of the mechanical status of each bearing<br />

and each gear. After a simple onsite analysis<br />

(ultrasonic listening) and a more detailed<br />

analysis (overall or static measurements and<br />

spectral or dynamic measurements) using<br />

UAS, pre-alarm, alarm and danger thresholds<br />

were assigned to each measurement point.<br />

This background work, which is required,<br />

allows technicians of the reliability department<br />

in charge of the measurements routes<br />

to get a quick overview of the asset hierarchy<br />

and immediately see the machines that have<br />

an alarm status.<br />

Display of the asset hierarchy including all rotating machines under monitoring<br />

and alarm statuses for each piece of equipment. In the present case, 19 rotating<br />

machines are monitored, 2 of which have exceeded the danger threshold for the<br />

bearings of the rear motor.<br />

ISSUE<br />

This preventive maintenance program has 3 objectives:<br />

• Highlight the efficiency of ultrasound measurements on rotating machines.<br />

• Issue a relevant diagnosis.<br />

• Offer preventive maintenance with reliable indicators.<br />

Experience feedback after onsite measurement sessions from<br />

October 2018 to November 2019<br />

Monitoring of a parallel reduction gear<br />

• Machine: Decanter – High-speed input bearing of the reduction gear<br />

Measurement carried out on 16/10/2018 Measurement carried out on 20/09/2019<br />

24 maintworld 1/<strong>2020</strong>


On the time spectra (same scale), we can observe the occurrence<br />

of shocks compared to the first measurement carried out<br />

in 2018. The trend curves show the evolution of the RMS static<br />

value: from -4.3 dBµV in 2018 to +16.8 dBµV in 2019.<br />

Based on SDT criteria, this increase corresponds to the early<br />

failure of a mechanical part of the reduction gear (bearings and/<br />

or gears).<br />

Zooming in on the FFT frequency spectrum allows highlighting<br />

dissymmetry of the modulation around the meshing frequency,<br />

which is characteristic of a damaged gear mesh.<br />

Listening to the bearing and analysing the frequency spectra<br />

(see chart above) has allowed confirming this diagnosis by observing<br />

the emergence of significant peaks associated with this<br />

gear damage.<br />

We can observe repeated shocks associated with the frequency<br />

of the high-speed input drive pinion of the reduction<br />

gear (24.93 Hz and its harmonics) with demodulation at each<br />

peak. Broken tooth and teeth clearance. Replacement of the reduction<br />

gear during a scheduled production shutdown, which<br />

avoided an untimely breakdown which could have generated<br />

significant expenses due to production losses.<br />

The endoscopic video inspection of the worm screw of the reduction<br />

gear confirmed the ultrasound diagnosis.<br />

The customer took the reduction gear down during a production<br />

shutdown.<br />

Wear detected on the tooth of the worm screw of a reduction gear,<br />

wheel and screw:<br />

• Machine: Lime mud filter agitator – High-speed input<br />

bearing of the reduction gear<br />

We can observe that between each revolution of the worm<br />

screw, there is a phenomenon occurring, which can be heard<br />

through the ultrasound detector as a sliding of the gear (worm<br />

screw/bronze wheel).<br />

Measurement carried out on<br />

10/07/2018<br />

Measurement carried out on<br />

29/10/2019. After replacement of<br />

the reduction gear<br />

Monitoring of the degradation of the bearing of a low-speed reduction<br />

gear (opposite transmission):<br />

• Machine: Vertex separator reduction gear – 4-train<br />

parallel reduction gear<br />

Measurement carried out on 11/12/2016<br />

From the beginning of the monitoring of this reduction gear<br />

(August 2018) using ultrasound technology, the occurrence of<br />

shocks can be observed on the time spectrum.<br />

1/<strong>2020</strong> maintworld 25


By zooming in on the time spectra, one can observe repeated<br />

shocks at 9.756 Hz (see the table of characteristic frequencies<br />

below) related to the frequency of the inner ring of the bearing<br />

of the low-speed reduction gear (opposite side of transmission).<br />

Diagnosis confirmed on the frequency spectrum (see below).<br />

Monitoring of the degradation of the bearing of a low-speed reduction<br />

gear (opposite transmission):<br />

• Machine: Lime mud filter – Bearing opposite transmission<br />

23140 CCK – 14.28 RPM<br />

Measurement carried out on<br />

16/10/2018<br />

Measurement carried out on<br />

07/01/2019<br />

The amplitude of the scaling-type defect is modulated by the<br />

rotation speed.<br />

On the spectrum, this is evidenced by a peak at the frequency<br />

of the defect of the inner ring of the output bearing of the reduction<br />

gear and by sidebands at the rotation frequency of the<br />

shaft, i.e., 0.5 Hz – 30 RPM (low-speed reduction gear).<br />

On time spectra (same scales), one can observe the occurrence of<br />

shocks right from the beginning of the monitoring of this bearing.<br />

After replacement of the bearing, all shocks disappeared. The<br />

rolling elements were no longer held in their housings.<br />

Trend curves show the decrease of the RMS value after servicing<br />

(from +10.7 dBµV in 10/2018 to -4.4 dBµV in 01/2019).<br />

Trend curves show the decrease of the RMS value after servicing,<br />

from +12 dBµV in 08/2019 to +1.2 dBµV in 11/2019.<br />

Measurement carried out on<br />

26/08/2019<br />

Measurement carried out on<br />

25/11/2019. After replacement<br />

of the reduction gear<br />

Replacement of the reduction gear during a scheduled production<br />

shutdown, which avoided an untimely breakdown which<br />

could have generated significant expenses due to production<br />

losses.<br />

Conclusion<br />

The challenge was taken up by the Reliability team of this paper<br />

mill. The implementation of a preventive maintenance program<br />

for 70 rotating machines has had a beneficial and decisive outcome.<br />

It will be extended to 100 other machines over the course<br />

of <strong>2020</strong>. SDT International offered a simple solution and suitable<br />

measurement tools, along with a LEVEL1 ASNT certified training<br />

program. Users have acquired a comprehensive mastery of<br />

this technology, which was new to them.<br />

1. Using ultrasound technology to monitor low-speed rotating<br />

machines, this paper mill was able to avoid a number of unscheduled<br />

shutdowns (see examples below) and highlight the complementarity<br />

of ultrasound and vibration technologies.<br />

2. Based on this experience, the Reliability department has<br />

decided to initiate ultrasound-aided greasing campaigns. Using<br />

suitable equipment (software and hardware), this acoustic lubrication<br />

program will ensure perfect greasing by indicating:<br />

• the right grease,<br />

• the right greasing location,<br />

• the right greasing interval,<br />

• the right quantity of grease to add,<br />

• the right indicators for the lubrication condition.<br />

Thus, full traceability of the lubrication program will be ensured.<br />

3. The versatility of ultrasound detector SDT270DU also allowed<br />

for the implementation of:<br />

• An energy-saving policy (detection of compressed air leaks,<br />

control of steam traps).<br />

• Control of tubeblowers (detection of leaks on steam valves).<br />

• Preventive maintenance of high voltage electric systems (corona,<br />

tracking, arcing).<br />

26 maintworld 1/<strong>2020</strong>

Be a LUBExpert<br />

®<br />


Poor greasing practices are<br />

a leading cause of bearing failure.<br />

Many lube departments re-grease on a wasteful<br />

calendar-based schedule. This leads to over and<br />

under greased bearings that fail to deliver their<br />

engineered value.<br />

LUBExpert tells us when to grease...<br />

and when to stop.<br />

Grease reduces friction in bearings. Less friction<br />

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friction levels increase, guides you during<br />

re-lubrication, and prevents over and under<br />

lubrication.<br />

Grease Bearings Right<br />

Right Lubricant<br />

Right Location<br />

Right Interval<br />

Right Quantity<br />

Right Indicators<br />

Ultrasound Soluons<br />




The Hidden Threat<br />

Text: Adash Ltd.<br />

You have done proper balancing (twice for sure), you have done alignment, you<br />

have checked the mountings and your machine is still vibrating like an old washing<br />

machine for no obvious reason… The reason is probably resonance.<br />

MECHANICAL RESONANCE is the tendency of a mechanical<br />

system to respond at a greater amplitude when the frequency<br />

of its oscillations matches the system's natural vibration frequency<br />

than it does at other frequencies.<br />

For every mechanical object, there are natural frequencies<br />

at which that object vibrates more easily and harder than at<br />

other frequencies.<br />

You can see this in everyday life. For example, there might<br />

be something in your car that does not vibrate very much, but<br />

if you make a small change in engine RPM, you can hear much<br />

stronger vibrations.<br />

This is an example of where the engine RPM is near the<br />

natural frequency of the thing that is vibrating.<br />

It is very dangerous to operate machines near their natural<br />

frequencies because even a small unbalance can generate extremely<br />

high vibrations. This can destroy the machine very easily.<br />

We usually encounter the resonance problem on the machine<br />

foundation. When the machine speed is near the natural<br />

frequency, then we measure high vibrations without a visible<br />

reason. The maintenance team usually carries out all the<br />

standard procedures like balancing, alignment and bearing<br />

mounting checks but vibrations still remain high. The resonance<br />

problem is the reason.<br />

Now I would like to describe to you how to use the bump<br />

test measurement. It is the perfect measurement for situations<br />

where you suspect a resonance problem. We used a steel beam<br />

for this simple demonstration. Imagine that it is a machine<br />

foundation.<br />

We used a standard 100 mV/g accelerometer and a hammer.<br />

The Adash VA4Pro and VA5Pro vibration analyzers contain<br />

a super easy bump test mode. There is no need for any settings.<br />

Just place the sensor on the object to be measured and hit it<br />

with a hammer.<br />

Let's look at the final graph. [1]<br />

High peaks on the spectrum represent natural frequencies,<br />

in our case 100 Hz and 280 Hz. There will be a resonance problem<br />

if the machine speed is near these two frequencies.<br />

We can move the natural frequency to solve the problem by<br />

reinforcing the construction in order to change the natural frequency.<br />

For example, we could add another pillar in the middle<br />

and the natural frequencies will decrease. [2]<br />

A fixed pillar would be welded to the machine foundation in<br />

28 maintworld 1/<strong>2020</strong>


Graph 1: initial spectrum<br />

Graph 2: changed spectrum<br />

1/<strong>2020</strong> maintworld 29


Figure 1. Every mechanical object has its mode shapes.<br />

Figure 2. The length of each arrow is<br />

proportional to the g value at that point.<br />

Figure 3. The first natural frequency is 10 Hz.<br />

Figure 4. Second natural frequency.<br />

the real world and natural frequencies would not only be<br />

reduced but also re-tuned.<br />

Every mechanical object has its mode shapes. I will<br />

explain it on this simple free beam (Figure 1).<br />

The first mode shape is this: we can see two nodes and<br />

three anti nodes. The next mode shapes have more nodes<br />

and more anti nodes. But for now, we will only consider<br />

the first one, whose natural frequency is the lowest<br />

natural frequency from the measured graph. We marked<br />

several points on our steel beam and measured the amplitude<br />

on each mark.<br />

The length of each arrow is proportional to the g value<br />

at that point. Now you can see the first mode shape,<br />

which we have got from real measurements (Figure 2).<br />

For simple objects you can calculate the mode shapes,<br />

but for a complicated construction it is not possible, you<br />

have to measure it.<br />

We analyzed the frame by hitting it with hammer.<br />

When the machine is operating, you can also measure<br />

the vibration levels at every point. In this case the frame<br />

is not excited by the hammer but by operational speed.<br />

The vibration levels will not be the same on all points of<br />

30 maintworld 3/2019

the machine. Measure all of them and draw the arrows<br />

again, you will get the first operational deflection shape<br />

of the machine. For finding the next operational deflection<br />

shapes, you have to know the spectrum of vibration<br />

on each point. Knowing the machine shapes is important<br />

for machine understanding.<br />

The ADASH analyzers contain the ADS mode. It enables<br />

you to measure these shapes very simply and then<br />

illustratively animate the results.<br />

In the next example I show you why it is important to<br />

know the mode shapes which are the cause of the vibration<br />

problem. We used the shaker and the rubber cord.<br />

The first natural frequency is 10 Hz and you can see the<br />

first mode shape (Figure 3).<br />

If I need to decrease the vibration level, then I can add<br />

the pillar to many places and it will work.<br />

But sometimes the second mode shape could be the<br />

problem rather than the first. Now you can see the second<br />

natural frequency (Figure 4).<br />

The location of the pillar is now much more important.<br />

If I add it in the middle, then the vibration remains<br />

unchanged.<br />

Back to our first example with the steel beam. Initial<br />

overall vibration was 6.34 g. We added a pillar in the middle<br />

and the main natural frequency on 100 Hz decreased<br />

approximately 4 times. But the second natural frequency<br />

on 280 Hz decreased only about 2 times and now is influencing<br />

our frame almost as much as the first frequency.<br />

The overall vibration is now 3.19 g.<br />

Then we moved the pillar from the middle to 1/3 of<br />

the way between the end pillars. The new natural frequencies<br />

look like this. [3]<br />

You can see that the first frequency remained the<br />

same, but the second was more reduced.<br />

Overall vibration decreased to 2.56 g.<br />

Graph 3: final spectrum<br />









How Predictive<br />

Maintenance<br />

Enhances<br />

Plant Safety<br />

without sacrificing productivity<br />


CMRP<br />

adrianm@uesystems.com<br />

Ultrasonic sensors<br />

improve safety by<br />

allowing assets to be<br />

inspected at a distance<br />

An effective predictive maintenance strategy<br />

leverages technology and analytic data to allow for<br />

optimized scheduling of corrective maintenance tasks.<br />

The goals of these programs are to reduce asset<br />

downtime, prevent unexpected failures, promote<br />

productivity and increase the safety of personnel.<br />

IN THE PAST, safety investments often<br />

meant a reduction in overall productivity<br />

or an increase in cost structure. However,<br />

modern technology has created<br />

new opportunities for facility managers<br />

to maintain or even increase productivity<br />

levels as the plant implements new<br />

safety protocols.<br />

How maintenance impacts<br />

facility safety<br />

Maintenance work can be dangerous,<br />

especially when assets fail in unexpected<br />

or catastrophic ways. In fact, a<br />

report from BLR (Business & Legal Resources)<br />

revealed that between 25 and<br />

30 percent of workplace deaths in the<br />

manufacturing industry are related to<br />

maintenance activities. Shocks, burns<br />

and injury from moving parts are all<br />

common causes of fatal accidents.<br />

A predictive maintenance strategy<br />

seeks to reduce or completely<br />

eliminate unexpected failures. Using<br />

technology such as ultrasound sensors,<br />

stakeholders can detect minute<br />

changes in sonic output to determine<br />

when an asset is likely to malfunction.<br />

Given enough data, stakeholders can<br />

determine expected life spans for all<br />

32 maintworld 1/<strong>2020</strong>


facility assets and build a maintenance<br />

schedule around this information. This<br />

way, maintenance personnel can power<br />

down assets during a period that is not<br />

likely to interfere with the facility’s<br />

productivity goals. They can complete<br />

maintenance work well before an asset<br />

begins to behave erratically, reducing<br />

the risk of severe injury.<br />

Combined with safety training and<br />

other fail-safes, a predictive maintenance<br />

program gives facility managers<br />

the ability to mitigate risk with precision.<br />

Predictive maintenance can improve<br />

asset uptime without sacrificing safety.<br />

Why predictive solutions<br />

optimize risk management<br />

Reactive maintenance programs carry<br />

a lot of financial risk. According to the<br />

International Society of Automation,<br />





manufacturers across all industries<br />

lose approximately $647 billion annually<br />

to asset downtime, thereby losing<br />

out on a corresponding $13 trillion in<br />

production value. To put those numbers<br />

in context, ISA explained that the<br />

average cement mill loses roughly $7<br />

million per year in lost production.<br />

Traditionally, matters of financial<br />

risk and safety risks have been a balancing<br />

act. More safety measures often<br />

meant fewer resources available for<br />

production. Predictive maintenance<br />

changes this paradigm significantly. In<br />

a predictive scheme, both productivity<br />

and safety increase with the strategic<br />

scheduling of maintenance tasks. The<br />

program not only allows maintenance<br />

staff to know when an asset is likely to<br />

fail, but also specifically how the asset<br />

is likely to deteriorate. Combined, this<br />

knowledge gives stakeholders an unprecedented<br />

strategic advantage.<br />

At the end of the day, safety issues<br />

and maintenance issues stem from the<br />

same problem: a lack of planning and<br />

analysis. Both types of risk are much<br />

easier to manage when stakeholders<br />

have the resources to plan for contingencies<br />

and eliminate the potential for<br />

risk before it manifests in a workplace<br />

accident.<br />

How technology improves<br />

safety and productivity<br />

An effective predictive maintenance<br />

strategy begins with good data. According<br />

to Control Engineering, this<br />

data can come from multiple sources,<br />

such as thermal readings and remote<br />

ultrasound sensors. The more performance<br />

indicators a facility manager<br />

has to work with, the more optimized<br />

the maintenance strategy.<br />

For example, say a facility manager<br />

knows one of his or her assets enters<br />

a fail state approximately every six<br />

months. The manager could schedule<br />

maintenance every five months to prevent<br />

failure. A smarter approach might<br />

be to place sensors along the moving<br />

parts of the asset and continually<br />

monitor decibel readings for changes.<br />

These sensors can show maintenance<br />

staff exactly which part of the asset<br />

is beginning to fail, so they can take<br />

targeted action in a timely manner.<br />

Over the years, the system would<br />

improve continually as stakeholders<br />

understand exactly when and how an<br />

asset needs to be serviced for optimal<br />

performance.<br />

Predictive maintenance requires<br />

forethought, strategy and intelligent<br />

technology investments.<br />

Sensors can be<br />

permanently<br />

mounted into<br />

an asset for an<br />

easy remote<br />

inspection<br />

The 4Cast monitors<br />

assets 24/7, taking<br />

decibel readings and<br />

sound recording<br />

1/<strong>2020</strong> maintworld 33


Root Cause of an Electrical<br />

Problem, Did You Find the<br />

Systematic Problem to Solve?<br />

Root cause failure analysis<br />

is a common term used<br />

within reliability and<br />

maintenance. Working<br />

with reliability and<br />

maintenance, we see<br />

organizations do root<br />

cause analyses but very<br />

few leads to corrective<br />

action and improvements.<br />

So why don’t we solve<br />

problems?<br />

A GOOD ROOT CAUSE elimination program<br />

needs a process, practical training<br />

in critical thinking, and coordination<br />

and follow-up of actions. IDCON has<br />

developed a process called Root Cause<br />

Problem Elimination (RCPE) where we<br />

emphasize solving the problem, documenting<br />

and executing tangible actions<br />

and visible results.<br />

I’ll give you a real-world example of a<br />

facilitated Root Cause Problem Elimination<br />

event.<br />

Wednesday at 08:42 AM Paper Machine<br />

#8 shutdown due to power loss in<br />

the press section. The paper machine<br />

was down for more than 9 hours before<br />

the circuit breaker was replaced and<br />

started up.<br />

The suggested approach is to use the<br />

same tools as in a murder investigation,<br />

because the circuit breaker was murdered!<br />

We started by collecting the data<br />

for the investigation. The data included:<br />

• Understanding how this equipment<br />

works<br />

• What happened, where, when, and<br />

similar objects<br />

• Changes in time – before, during<br />

and after the paper machine shut<br />

down?<br />

• Physical evidence - gathered all<br />


Senior Management<br />

Consultant with<br />

IDCON INC.<br />

Figure 1: Failed Power Circuit break for<br />

Paper machine press section.<br />

Figure 2: The How Can diagram using<br />

Post-it® notes. All you need is a wall, no<br />

software needed.<br />

the parts of the breaker, took pictures,<br />

and did a forensic analysis<br />

• Conducted interviews with personnel<br />

involved<br />

As mentioned earlier we train people<br />

to eliminate problems using critical<br />

thinking tools and a structured method.<br />

Much like RCA, the process starts with<br />

the trigger. In this case, the trigger was<br />

defined as<br />

“machine downtime event >30-minutes<br />

then initiate formal root cause analysis”<br />

The next step is to clearly state the<br />

problem. The problem statement should<br />

follow the rule “one object and one problem”<br />

in this case “circuit breaker to press<br />

section shorted”<br />

The problem was now identified. The<br />

next step was to determine “How Can”<br />

the circuit breaker short. To find alternatives<br />

that may have caused the problem,<br />

we use a tool called the How-Can Diagram<br />

(Figure 3).<br />

Start from the right, spell out the trigger,<br />

problem statement and then you<br />

ask the questions “how-can the circuit<br />

breaker short? Answers “phase to phase”<br />

or “phase to ground”<br />

Next question How-Can this short<br />

happen? “Dust on the terminal connects<br />

(stabs)” or “Corrosion on the terminal<br />

connections (stabs)” and keep going until<br />

you have exhausted the alternatives.<br />

Most important, provide all alternatives<br />

that make sense but don’t jump to conclusion.<br />

The best way to start the creative<br />

process is not to use a spreadsheet or software<br />

but post-it notes on the wall. The<br />

post-it notes engages the problem-solving<br />

team and keeps them actively involved –<br />

trust us on this one!<br />

Next step is to check the facts and find<br />

the most likely cause, in this way you can<br />

eliminate or confirm parts of the How-<br />

Can diagram. State each fact for the each<br />

of the boxes in the How-Can Diagram and<br />

ask the question “This is a fact because?”<br />

34 maintworld 1/<strong>2020</strong>


Figure 3: The How-Can diagram for<br />

this investigation<br />

Example the dust and dirt on the terminal<br />

connects (stabs) is a fact because we could<br />

see it and confirmed that it was present<br />

on other circuit breakers too. We could<br />

not confirm that there was any mechanical<br />

damaged or alignment problem of the<br />

terminal connects (stabs) of the circuit<br />

breaker. The Electrician and Electric Engineer<br />

confirmed that the arcing occurred<br />

between the breaker phase terminal<br />

connects (stabs) and not to ground. The<br />

investigation confirmed that the Technical<br />

Cause for the breaker to short is the<br />

missing dust and chemical filters.<br />

There were other causes we needed<br />

to figure out – How Can a filter not be in<br />

place? What we found was that the filters<br />

weren’t in stock because they weren’t approved<br />

for purchase! The cost for the filters<br />

were $50K each. It was deemed too<br />

expensive to the maintenance budget.<br />

The Human Cause is that the maintenance<br />

team should have known the<br />

impact of not replacing the filters to save<br />

$100k. The decision to save $100K on the<br />

filters, ended up costing them a lot more<br />

to repair the “murdered” circuit breaker.<br />

The Systematic Cause is the lack of<br />

training for how the filters impact the<br />

long-term reliability of the MCC room<br />

electrical equipment. The other systematic<br />

issue, changes to the equipment<br />

should be reviewed and approved by the<br />

qualified technical expert through the<br />

Management of Change process before<br />

being implemented.<br />

Eliminating the problem<br />

The RCPE team presented the investigation<br />

to the mill leadership and put a<br />

plan in place that would eliminate the<br />

systematic cause, the human cause and<br />

the technical cause. A training plan was<br />

put in place so that everyone understood<br />

how important the filters were to the reliability<br />

of the plant and a MOC process<br />

was put in place ensuring that no changes<br />

were made without proper approval.<br />

Reveal Your Potential<br />

Get a Reliability and Maintenance Assessment<br />

Call us +1 919-847-8764

EVENT<br />

The Day After Tomorrow<br />

in Asset Performance<br />

Peter Hinssen is an<br />

entrepreneur, who has<br />

focused on startups for<br />

almost 20 years. He is<br />

a technologist at heart.<br />

Peter will be talking at the<br />

Asset Performance 4.0<br />

Conference on September<br />

16th in Ghent about how<br />

companies and technical<br />

services can prepare for<br />

the future.<br />






It is a little bit personal because my father<br />

worked in the oil and gas industry<br />

his entire life, specifically Maintenance<br />

and everything that deals with process<br />

control. So, when I was a kid, it was all I<br />

heard from my dad coming home. And I<br />

think the evolution that you see in this<br />

industry is fascinating. New technologies<br />

are changing, in my opinion, tremendously:<br />

dealing with assets, managing<br />

performance and thinking about prediction<br />

is going to change tremendously.<br />

So, I am very excited to be part of this.<br />

Can you give some examples of technologies<br />

that will impact our world?<br />

Big Data. I mean, this is an industry<br />

that has always been interested in information.<br />

But now Big Data is becoming<br />

abundant. We have technologies to deal<br />

with that. We have machine learning,<br />

artificial intelligence, all these mechanisms<br />

of connectivity. I think if you put<br />

it all together, it is piling up technology<br />

upon technology that is fundamentally<br />

changing how we think about how to<br />

deal with data. And I think it will have a<br />

tremendous impact on this industry.<br />


Author of The Day<br />

After Tomorrow and<br />

keynote speaker at the<br />

Asset Performance 4.0<br />

Conference in Belgium.<br />



We are currently in a disruptive era. I use<br />

this word carefully, but it indicates a constant<br />

acceleration and the need to follow<br />

that speed. Lots of companies see a huge<br />

conflict between possibilities and reality.<br />

So this gap and tension between what is<br />

possible and what you do day-to-day is<br />

a big challenge. We need to take a huge<br />

leap in skills and technology. This is also<br />

an opportunity to become more critical<br />

of your company. Performance plays an<br />

important role. And the reason why your<br />

company exists is absolutely core. But be<br />

careful what you wish for, because once<br />

you enter the spotlight, you have got to<br />

deliver. Take up your role and realise it.<br />



Being able to tell the story and carry it<br />

out. Storytelling is key. IT people should<br />

be rockstars, but most of the time they<br />

are not so communicative. That is because<br />

they don't have the skills to tell<br />

their story. If you go from predictive<br />

maintenance to Asset Performance in a<br />

connected world, then you have so many<br />

touch-points, that you have to broaden<br />

your gaze. You need more skills and<br />

competences. Your suppliers change.<br />

Your partners change. And everything<br />

becomes more fluid.<br />





Well, I have a very simple idea of how<br />

much time companies spend on today,<br />

tomorrow, and the day after tomorrow.<br />

Most companies are very busy with today.<br />

And when they look at the future,<br />

they often extrapolate today, they think<br />

that tomorrow is approximately the<br />

same. But we are now facing so many<br />

different changes that there might be<br />

changes in business models or in technologies<br />

or new players coming onto the<br />

market. We have to think about this disruption,<br />

'this is the day after tomorrow',<br />

and how you deal with that. When I talk<br />

about today, tomorrow, and the day after<br />

tomorrow, many people say they dedicate<br />

70-20-10 percent of their time on it.<br />

The reality is we spend 93 percent of our<br />

time on today, maybe 7 percent thinking<br />

about tomorrow and virtually none<br />

on the day after tomorrow. And I think<br />

in many industries, this was okay and in<br />

the 20th century. But we are now fully in<br />

the 21st century. That doesn't work anymore.<br />

We have to be much more flexible<br />

and agile. And that is why the day after<br />

tomorrow is more important than ever<br />

before.<br />





Of course you want to be essential. You<br />

want to do something that makes sense.<br />

If you do predictive maintenance, that<br />

is essential. If you work for customers,<br />

you are hoping that you are vital for that<br />

customer. But the other question is, how<br />

relevant are you? And I think there is a<br />

very clear difference between essential<br />

and relevant. Think about the telecom<br />

industry. If you look at telecom 10 years<br />

ago, a telecom operator was essential.<br />

You needed a SIM card, and they were<br />

relevant, they gave you added value. Today,<br />

in this world, they are still essential,<br />

because you still need that SIM card. But<br />

the relevance has dropped. And therefore,<br />

if whatever capacity you have in an<br />

36 maintworld 1/<strong>2020</strong>

EVENT<br />

Asset Performance 4.0<br />

Conference & Exhibition<br />

• Asset Performance 4.0 Conference,<br />

15-17 September <strong>2020</strong>,<br />

ICC Ghent, Belgium<br />

• More info and registrations :<br />

www.assetperformance.eu<br />

organization, whatever position you have<br />

in dealing with the outside world, that<br />

is the core question, are you essential? I<br />

hope you are. But how can you make sure<br />

that your relevance doesn't go down?<br />









Well, I think it is that we are in an age<br />

where everything is interconnected.<br />

So, if you look at an organization, they<br />

are not silos anymore. We are in this<br />

network age, everything is connected<br />

to everything else. So when you say that<br />

your customer doesn't pay for the maintenance<br />

directly, that is true, but your<br />

customer will feel, see and understand<br />

whether this is something which is integral<br />

in terms of quality thinking or performance<br />

management. And in the end,<br />

if your company wants to be flexible and<br />

fast and agile, you have to incorporate<br />

that into every part of the organization.<br />

Every fibre, every node, every element<br />

has to understand that you are part of<br />

a bigger picture, and you have to keep<br />

reinventing yourself to be both essential<br />

and relevant for the outside world.<br />









Well, I think we are in a phase where a<br />

lot of the technologies emerging take<br />

something like AI or machine learning<br />

that is relatively new. Most people<br />

don't understand it very well, there<br />

is a huge skill gap that we need to fill,<br />

because we need to train and prepare<br />

people for that. But a lot of things are<br />

just trying out, companies are experimenting<br />

and figuring out how to apply<br />

this, but it is very early in the game.<br />

If you compare that to the PC industry,<br />

this was the time where we had<br />

Commodores and Ataris, and not the<br />

established industry like we have it<br />

today. We are going through that phase.<br />

And if you are too early, you are going<br />

to burn a lot of money and not get a<br />

lot of results. But if you wait too long,<br />

you probably run the risk of becoming<br />

completely obsolete. I think it is making<br />

sure that you are constantly in tune that<br />

you are constantly alert that you follow<br />

this as closely as possible and make the<br />

right move at the right time. But you<br />

can only do that if you are prepared.<br />



I think they have to make time for it.<br />

Time is the biggest issue. To put your<br />

day-to-day work at the side is difficult,<br />

but you really have to invest in these<br />

skills. Experiment at first, like tinkering<br />

with fuel until somethings blows<br />

up. There are so many possibilities,<br />

also online, to test and try out different<br />

stuff. Everybody talks about life long<br />

learning, but most managers don't do it<br />

themselves.<br />



4.0 CONFERENCE?<br />

I think this is one of the most fascinating<br />

industries that for a long time, has<br />

already worked with data. But it's now<br />

making a quantum leap. I'd love to talk<br />

about how I see that evolving, I hope to<br />

inspire you to maybe even do more than<br />

what you're doing today. But above all,<br />

to prepare ourselves for, I think, a very<br />

disruptive wave that is going to affect<br />

everyone. And I think if we understand<br />

this, we can all actually come out even<br />

better as a result.<br />

1/<strong>2020</strong> maintworld 37


A standardised methodology<br />

with factory specific outcome<br />

Multi-site approach with VDM XL<br />

Every factory is unique. Think of differences in<br />

product and manufacturing process, the technical<br />

condition of the assets or the way we do maintenance.<br />

Then it appears to be impossible to implement one<br />

standardised improvement method still enabling each<br />

Technical Services Department to add value to the<br />

operating result. It is possible though, with VDM XL .<br />

VDM XL STANDS for Value Driven Maintenance<br />

and Asset Management. VDM XL<br />

explains how to extract maximum economic<br />

value from an existing plant, fleet<br />

or infrastructure using a professional<br />

management approach. This worldwide<br />

recognised method was developed by<br />

Mark Haarman and Guy Delahay from<br />

consultancy firm Mainnovation. With<br />

this methodology capital-intensive companies<br />

can professionalise their Technical<br />

Services Department and transform<br />

it from a cost centre into a business<br />

function that continuously improves<br />

business performance.<br />

Customisation<br />

What do you need, to improve Maintenance<br />

& Reliability in your organisation?<br />

How to create value with asset<br />

management? How do you manage your<br />

maintenance organisation and make<br />

sure your decisions benefit the company?<br />

“The answers to these questions<br />

vary per factory”, says Mark Haarman,<br />

managing partner from Mainnovation.<br />

“We have applied the VDM XL method in<br />

factories in various industries like Food<br />

& Beverage, Life Science and (Petro)<br />

Chemicals. But also in Energy & Utilities<br />

and Fleet & Transportation you need<br />

to manage your assets, preferably in a<br />

way that creates value for the company.”<br />

Haarman explains how every factory,<br />

every client has specific needs because of<br />

the uniqueness of the assets. “Working<br />

with a standardised method and making<br />

sure that every specific factory executes<br />

maintenance in the same way, seems impossible.<br />

But we provide a solution with<br />

VDM XL . Our standard approach can be<br />

customised when it comes down to implementing<br />

improvements. We present<br />

a plant-specific solution, with a focus on<br />

the most dominant value driver per factory.<br />

VDM XL is a standardised methodology,<br />

but the outcome is always factory<br />

specific.”<br />

Value drivers<br />

VDM XL distinguishes four axes on which<br />

the Maintenance & Asset Management<br />

organisation can add value with an existing<br />

installation. They are called the four<br />

value drivers. Haarman: “With an audit<br />

we can measure the current performance<br />

and maturity levels of the Technical<br />

Services Department and determine<br />



varies per factory. “But every factory<br />

can start with the same method”,<br />

Haarman explains. That's why VDM XL<br />

is the right decision for a multi-site<br />

approach. The method has already<br />

proven itself at large companies with<br />

multiple site locations. “By using one<br />

method, it's possible to compare sites,<br />

while each factory derives its own<br />

action plan. A strategy that creates<br />

value per factory and creates opportunities<br />

for continuous improvement.<br />

And I can tell you: that makes both<br />

senior management and the Technical<br />

Services Department happy.”<br />

what would be the winning Maintenance<br />

and Asset Management strategy for the<br />

future. This strategy describes the improvements<br />

in processes, organisation,<br />

IT systems, data and performance management<br />

needed to create maximum<br />

economic value for the business. Now<br />

we know on which value driver we need<br />

to focus: should the Technical Service<br />

Department be managed based on cost<br />

reduction, increased uptime, safety improvement<br />

or lifetime extension?”<br />


☏ +31 (0)78 614 67 24<br />

info@mainnovation.com<br />

www.mainnovation.com<br />

38 maintworld 1/<strong>2020</strong>


Viewing Maintenance<br />

as a System to<br />

Optimize Performance<br />

Tracy T. Strawn,<br />

Strategic Advisor,<br />

Marshall Institute<br />

In 1958, Mao Zedong of China ordered all sparrows killed because they were eating<br />

grain necessary for people, and he thought killing the sparrows would result in<br />

surplus food for 60,000 people. This campaign seemed successful, as the sparrow<br />

was nearly made extinct in China. Unfortunately, Mao did not realize that sparrows<br />

were natural predators to locusts and other insects. With the sparrows gone, the<br />

locusts multiplied, devastating Chinese agriculture. The ecological imbalance helped<br />

spur on massive food shortages and the death of an estimated 30 million people.<br />

WHY DID THIS HAPPEN? Mao didn’t have an appreciation of a<br />

system which maintained ecological balance. Once upset, the<br />

unintended consequences resulted in millions of deaths.<br />

Ecological systems and their mismanagement are only one<br />

facet of the study of systems. Systems or systems theory is an<br />

interdisciplinary field that studies the nature of systems—from<br />

simple to complex—in nature, society, engineering, technology<br />

and science. Some areas of study include systems engineering,<br />

systems analysis and systems thinking. This article will focus<br />

specifically on how systems concepts apply to the maintenance<br />

organization and why we should be concerned about it.<br />

What is a system?<br />

• A system is a group of interacting or interrelated entities<br />

that form a unified whole. A system is delineated by its<br />

spatial and temporal boundaries, surrounded and influenced<br />

by its environment, described by its structure and<br />

purpose and expressed in its functioning. Systems are<br />

the subjects of study of systems theory. ¹<br />

From this we can assume a system is comprised of smaller subsystems<br />

with a purpose or goal. Systems science can apply to a<br />

business or organization.<br />

• Blanchard defined system as “…a set of interrelated components<br />

working together with the common objective of<br />

fulfilling some designated need”. 2<br />

When applied to businesses, we can conclude that these “components”<br />

are working together to produce something of value<br />

to the user, and that their efficiency and effectiveness are<br />

dependent on how well they work together. If a system is inadequately<br />

designed or poorly connected and integrated, it most<br />

likely will be unable to achieve its goals.<br />

Symptoms of broken systems:<br />

• Silo mentality: an inward mindset that resists sharing<br />

information/resources with others<br />

• Processes plagued by waste and inefficiency due to poor<br />

workflow design, broken customer/supplier relationships<br />

and poor decision making<br />

• Variability in meeting customer requirements due to<br />

poor system design and standardization<br />

• Components or subsystems of an organization are not<br />

aligned with a clear purpose, characterized by inability<br />

to execute strategy and lack of understanding of what<br />

matters to performance.<br />

•<br />

These symptoms can lead to financial ruin.<br />

Business systems are frequently identified by department<br />

names: Procurement, Engineering, Finance or Human Resources.<br />

Their workflows, connectivity and relationships result<br />

in producing something of value. If their workflows, connectivity<br />

and relationships are optimum, the value they produce<br />

should meet or exceed the goals of the business.<br />

Systems and processes are the essential building blocks of<br />

a company. Every facet of business - storeroom, workshop,<br />

production - is part of a system that can be managed to produce<br />

something of value. The details of each business system vary<br />

by company, but the fundamentals remain the same.<br />

¹ Wikipedia, Systems<br />

² Systems Engineering Management, 2nd Edition, B. Blanchard, 199<br />

40 maintworld 1/<strong>2020</strong>


The Key Concepts of Systems:<br />

1. Systems are composed of interconnected parts<br />

2. Inter-connected parts are interdependent<br />

3. Every system has an aim<br />

4. The structure of a system determines its behavior<br />

5. How well the parts cooperate to support the aim determines<br />

how efficient and effective the system functions<br />

6. Ultimately we seek synergy in which “the whole is greater<br />

than the sum of the parts<br />





PLANT<br />


Is Maintenance a System?<br />

Based on definitions, maintenance is a system: usually a subsystem<br />

of the corporation, made up of a collection of elements<br />

organized to achieve a purpose. How well the elements are integrated<br />

and interact determines the efficiency and effectiveness<br />

of the maintenance system.<br />

Figure 1 is a simple depiction of a system view of maintenance.<br />

The maintenance objective will reflect and align with<br />

corporate and plant objectives, considering the plant structure,<br />

while defining equipment strategies for ensuring the level of<br />

reliability required. Equipment strategies will define spares<br />

policy and outline resource structure needed to plan, schedule<br />

and execute the workload. The administrative structure will<br />

define and support personnel policies and determine budgets/<br />

controls to manage costs. Subsystems will be reviewed/improved<br />

to optimize their contribution to the systems aim and<br />

goals: achieving the desired level of reliability while meeting<br />

cost and safety targets.<br />



ADMIN<br />






PLANT<br />




1/<strong>2020</strong> maintworld 41






Figure 2 depicts maintenance as a system. This example<br />

shows how three sub-systems working as a system contribute<br />

to achieving optimum inherent availability. Three subsystems<br />

in this example are materials management, resource management,<br />

and reliability management. Materials management<br />

and resource management aid in optimizing the repair process.<br />

Both subsystems are critical in identifying and procuring<br />

spares required for the repair, planning/scheduling the repair,<br />

and mobilizing the resources to complete the repair. To ensure<br />

repair is made in the shortest time and at the lowest cost,<br />

information and communication must flow seamlessly and<br />

continuously between the two subsystems. The metric used to<br />

measure the repair process is ‘mean time to repair’ or MTTR.<br />

The third subsystem in the maintenance system example<br />

is reliability management, responsible for establishing the<br />

equipment strategies for achieving optimum reliability while<br />

considering safety and cost. Equipment strategies are defined<br />

by manufacturers’ recommendations or a risk based approach<br />

like reliability centered maintenance. The metric used to<br />

measure reliability performance is ‘mean time between failure’<br />

or MTBF. MTTR and MTBF will be used as illustrated in Figure<br />

2 to calculate inherent availability.<br />

In order to realize the goal, achieving the desired inherent<br />

availability, all three subsystems must work together.<br />

Achieving a systems aim must be managed with attention to<br />

the entire system. When we optimize subcomponents of the<br />

system, we don’t necessarily optimize the overall system. Suboptimization<br />

is the practice of focusing on one part of a system<br />

and making changes intended to improve that subsystem while<br />

ignoring the effects on other subsystems. This will lead to suboptimization<br />

of the whole system leading to waste, delays, and<br />

inefficiencies resulting in lost profits and lower plant throughput.<br />

Optimizing system performance should begin in the system<br />

design phase.<br />

8 Characteristics of a Good System Design:<br />

1. Designed with the customer (internal and external) in mind<br />

• Work products and services are handed off to internal<br />

customers who must meet their requirements.<br />

The objective is to prevent the internal customer<br />

from reworking or worse, passing through product<br />

that is fails to meet specifications.<br />

2. Represents your best known way of doing something<br />

• A well defined system should be documented with<br />

workflows of each subsystem and where the work is<br />

performed and handoffs are made, clearly describing<br />

roles and responsibilities.<br />

3. Has one primary aim, goal or purpose<br />

• Primary/secondary goals should be clearly defined<br />

and communicated to stakeholders.<br />

4. Has an owner, accountable for/reporting on system results<br />

5. Is as simple as possible, documented, understood by<br />

workers, and repeatable<br />

• System users should be trained/coached on subsystems<br />

and processes.<br />

6. Has performance standards and results are measured.<br />

• System should be thoroughly implemented and capable<br />

of meeting performance standards and goals.<br />

System users understand performance standards<br />

and can identify out of compliance conditions. Measures<br />

are monitored by all team members.<br />

7. Workers get ongoing feedback about system performance<br />

and are recognized for good results.<br />

8. Has sufficient focus on system details to eliminate most<br />

bottlenecks, inefficiencies, waste, and rework.<br />

Optimizing Maintenance System Performance<br />

These steps will aid in optimization of existing maintenance<br />

organizations and system with every process having an input<br />

from a supplier and an output to a customer. Process stakeholders<br />

understand how they add value to the goal of the system.<br />

A robust system will have clear specifications for each<br />

product handed off to internal customers along with feedback<br />

to suppliers.<br />

1. Process map and document your system<br />

• Identify your processes<br />

• Show how the processes work together to produce<br />

value and their interconnectivity<br />

• Ensure roles and process steps are clear<br />

2. Identify your products and services for each process<br />

3. Understand customer/supplier specifications/requirements<br />

4. Identify the suppliers/customers for each product/service<br />

5. Communicate process requirements to suppliers<br />

6. Identify customer specifications<br />

7. Demonstrate the process is capable/meets specifications<br />

8. Achieve a thorough implementation<br />

9. Continuously improve<br />

Summary<br />

The complexity of maintenance systems increases as new technologies<br />

are introduced. In today’s environment, there is an<br />

increasing need to develop/produce systems that are robust, reliable,<br />

high quality, supportable and cost effective. Viewing and<br />

understanding maintenance as a system with an aim and purpose,<br />

rather than a collection of disparate parts, is the first step<br />

in designing and developing a maintenance system that can be<br />

managed and optimized for sustained long term performance.<br />

42 maintworld 1/<strong>2020</strong>


Use of High-speed<br />

Thermography<br />

in Laser High-temperature<br />

Capillary Gap Brazing<br />

Lasers are extremely versatile tools in industry and manufacturing technology.<br />

Due to their flexibility, they serve as a key technology for implementing<br />

the goals of industry 4.0. Although laser cutting and welding are nowadays<br />

regarded as turnkey technologies, most laser applications, for example<br />

joining of hybrid materials, 3D printing or ultra-short pulse processing,<br />

still require considerable research and development.<br />

Text: M. HOFELE, D. KOLB, S. RUCK, H. RIEGEL, LaserApplikationsZentrum of the Hochschule Aalen; InfraTec GmbH Infrarotsensorik und Messtechnik<br />



of Aalen University intensively researches<br />

and develops new methods of laser<br />

material processing. Thus, innovative<br />

materials for Additive Manufacturing<br />

are developed and investigated within<br />

public R&D projects, including magnetic<br />

materials or electrical energy storage materials<br />

for electromobility. Another focus<br />

is lightweight construction. Here, among<br />

other things, mixed metallic compounds<br />

and hybrid lightweight structures<br />

made of aluminium and CFRP for CO₂efficient<br />

mobility concepts are investigated.<br />

The newly developed processes<br />

aluminium laser polishing and high-temperature<br />

capillary gap brazing are already<br />

being used in industrial projects.<br />

Making Heat Flow Visible<br />

Laser processes are highly dynamic<br />

thermally induced processes that cannot<br />

be detected with the naked eye.<br />

High-speed visual cameras that are<br />

often used are not capable of visualis-<br />

ing the heat flow in the component. A<br />

contacting temperature measurement<br />

of the moving very small liquid metal is<br />

not possible. In addition, the processing<br />

zone should remain free of influences<br />

from the measurement system. This is<br />

exactly what thermographic cameras<br />

do, which provide high frame rates with<br />

high spatial resolutions at the same<br />

time.<br />

Specially Configured<br />

Thermographic Camera within<br />

Laser Material Processing<br />

Laser-based production involves special<br />

requirements on the use of a thermographic<br />

camera. One reason for this are<br />

the processing temperatures of typically<br />

500 °C to 2,000 °C. In addition, the components<br />

of the camera must be protected<br />

against sputters, caused by the machining<br />

process. If the laser manufacturing<br />

processes take place in process chambers<br />

under a specific atmosphere, the<br />

measurement section is enriched with a<br />

gas. Especially the optics of the camera<br />

must be protected from the reflected<br />

laser radiation. It is therefore equipped<br />

with a laser protection lens for solidstate<br />

lasers and a filter for through glass<br />

and high-temperature measurement.<br />

Due to these precautions, the camera can<br />

be used near the laser beams without any<br />

problems.<br />

Configured appropriately, the thermographic<br />

camera ImageIR® 8300 hp<br />

from InfraTec supports the LAC with<br />

its high spatial and temporal resolution.<br />

Using the camera's MicroScan function,<br />

images can be taken with a spatial resolution<br />

of more than one megapixel. The<br />

10 GigE interface allows fast data transmission<br />

up to 355 Hz in full frame mode.<br />

Due to the optical package consisting of<br />

a telephoto lens with 50 mm focal length<br />

and the close-up lens for reducing the<br />

minimum focusing distance down to 170<br />

mm, the researchers can easily adapt the<br />

camera to changing working distances<br />

and sizes of the measured objects.<br />

1/<strong>2020</strong> maintworld 43


Laser camera: ImageIR® 8300 hp<br />

Fig. 1 (a) Test setup in laser cell<br />

TLC 1005, (b) 3D model process<br />

chamber, (c) Dimensions of<br />

solder sample<br />






Analysing the Temperature<br />

Control Behaviour During<br />

Laser Beam High-temperature<br />

Soldering<br />

The LAC of Aalen University, together<br />

with its industrial partner conntronic<br />

Prozess- und Automatisierungstechnik<br />

GmbH from Augsburg, is investigating<br />

laser high-temperature capillary gap<br />

brazing of corrosion-resistant steels<br />

for tube assemblies in automotive and<br />

mechanical engineering within the<br />

framework of the publicly funded research<br />

project “enAbLe”. In contrast to<br />

induction and furnace brazing, the laser<br />

beam serves as a flexible and highly efficient<br />

tool. The challenge lies on the one<br />

hand in the required highly pure reduction<br />

process environment to remove<br />

the oxide layers for good wetting of the<br />

copper solder and on the other hand<br />

in the homogeneous tempering of the<br />

joining zone. For homogeneous heating,<br />

the laser beam is controlled to the<br />

desired process temperature of 1,300 °C<br />

by means of a coaxially integrated highspeed<br />

pyrometer with sampling rates of<br />

several kilohertz. In addition to temperature<br />

control, the exposure strategy has<br />

a decisive influence on the formation of<br />

the temperature zones. Besides the FEM<br />

simulation, the thermographic camera<br />

is used for process development in the<br />

empirical experiments.<br />

The experiments take place in a sixaxis<br />

TLC 1005 laser cell with an infrared<br />

4 kW disc laser TruDisk 4002. The test<br />

geometry consists of a tube plug connection,<br />

austenitic chrome-nickel steel<br />

1.4301, with outer tube diameters of 10<br />

mm and 7.9 mm. Three weld spots offset<br />

by 120° fix the pipe-plug connection. The<br />

solder is pure copper from Voestalpine<br />

in the form of a solder ring (Fig. 1c). The<br />

oxygen-reduced process chamber used<br />

for the tests has a laser-permeable beam<br />

entry window in the lid. The soldering<br />

tests are carried out in a forming gas atmosphere<br />

with a residual oxygen content<br />

of less than 150 ppm. The atmosphere is<br />

monitored by means of a residual oxygen<br />

measuring device. During the process,<br />

the soldering assembly, clamped in a<br />

three-jaw chuck, rotates around the pipe<br />

axis by an external rotary machine axis.<br />

The laser beam is defocused to a diameter<br />

of 9 mm and radially tempering the<br />

outer surfaces of the joining zone (Fig.<br />

1b). The beam centre is oriented centrically<br />

to the solder ring.<br />

The laser soldering process is divided<br />

into three phases: heating, soldering with<br />

component rotation and cooling. During<br />

the heating phase of 10 s (Fig. 2 upper<br />

row), the laser heats the facing component<br />

surface to the control temperature<br />

of 1,300 °C in a stationary manner. During<br />

the soldering phase (Fig. 2 middle<br />

row) the assembly performs a complete<br />

rotation with an angular speed of 540 °/<br />

min. After the copper solder depot has<br />

been molten, the solder gap filling starts<br />

at 31.9 seconds. Due to the lower emission<br />

of copper, the forming fillet appears<br />

cooler than the surrounding steel surface<br />

of the joining partners. Because of the very<br />

good thermal conductivity of the solder,<br />

the through heating of the inner tube also<br />

starts at this point (difference between<br />

picture at 16.3 s and picture at 31.9 s in<br />

Fig. 2). At the end of complete rotation<br />

and shutdown of the laser, the component<br />

cools to below 600 °C within 18 s (Fig. 2<br />

lower row).<br />

The measurement data of the thermographic<br />

camera allows a versatile process<br />

analysis afterwards. The diagram in figure<br />

3 shows the temperature-time sequence<br />

of two measurement points, P1 in the laser<br />

spot centre and P2 on the inner tube. The<br />

dynamic temperature changes in the gap<br />

filling process stand out in this context.<br />

Fig. 2 Process sequence for laser beam high-temperature capillary gap soldering of a<br />

tube plug-in connector<br />

44 maintworld 1/<strong>2020</strong>


Fig. 3 Analysis of the temperature-time sequence on the basis of the thermal image data<br />

Fig. 4 Cutted joining zone of a lasersoldered<br />

tube-plug-in connection in<br />

longitudinal direction.<br />

Figure 4 shows the longitudinal<br />

cross section through the faultless laser<br />

soldered tube plug connection. The presented<br />

sample shows a complete gap filling<br />

without porosity. The evenly formed<br />

grooves on both sides ensure a good<br />

distribution of force and low-turbulence<br />

flow inside the pipe.<br />

Due to the evaluation of further thermographic<br />

data, the temperature control<br />

strategy could be further optimised<br />

and soldering times of less than 10 s<br />

could be realised.<br />

Collecting Important Process<br />

Knowledge<br />

Regarding laser soldering, thermography<br />

provides important insights into<br />

process development. Thus, optimised<br />

path planning for optimal heating can<br />

be identified and at the same time<br />

the thermal load of the surrounding<br />

zones can be reduced. The LAC aims<br />

to achieve comparable results with<br />

similar tasks. With the help of the thermographic<br />

camera, for example, the<br />

temperature distribution gets recorded<br />

in each layer at the 3D metal printing<br />

process of selective laser melting. The<br />

focus lies primarily on the heating and<br />

cooling behaviour of the used materials,<br />

which has a direct effect on the<br />

structure quality that is formed.


Predictive Maintenance:<br />

The Wrong Solution to the<br />

Right Problem in Chemicals<br />


Chemicals plants often<br />

have plenty of good data<br />

on equipment performance<br />

and reliability. A predictivemaintenance<br />

program<br />

might be the worst way to<br />

use it.<br />


others, there is considerable excitement<br />

about the potential of advanced predictive-maintenance<br />

(PdM) approaches.<br />

The promise of these new techniques<br />

is tantalizing. Using machine-learning<br />

technologies to comb through historical<br />

performance and failure data, they<br />

aim to tell operators when and how a<br />

component is likely to go wrong in the<br />

future with a high level of predictability.<br />

This should reduce the impact of equipment<br />

failures—and the cost of efforts to<br />

prevent such failures—by turning inefficient,<br />

unplanned maintenance activities<br />

into efficient, planned ones.<br />

At first sight, chemicals plants seem<br />

like the ideal environment for PdM.<br />

High levels of automation and instrumentation,<br />

combined with rigorous<br />

maintenance record-keeping, create the<br />

rich data that machine-learning systems<br />

require. Moreover, most plants strive for<br />

stable operating conditions, potentially<br />

making it easier to spot patterns and<br />

trends. There’s also a compelling business<br />

case for improved reliability. Overall<br />

equipment effectiveness (OEE) losses<br />

due to unplanned maintenance range<br />

from 3 to 5 percent across the industry.<br />

Predicting Poor Results<br />

Take a closer look, however, and the<br />

potential of PdM in chemicals begins<br />

to evaporate, for four main reasons.<br />

AUTHORS:<br />

WIM GYSEGOM is a partner<br />

in McKinsey’s London office, where<br />

SVEN HOUTHUYS is an associate<br />

partner, and JOEL THIBERT is an<br />

associate partner in the Santiago office.<br />

• TOO LITTLE DATA. In a chemicals<br />

plant, predicting failures is harder<br />

than it first appears. Unplanned<br />

downtime is typically concentrated<br />

in a small number of large events.<br />

That means there are typically too<br />

few datapoints for PdM systems to<br />

learn from.<br />

• TOO LITTLE TIME. Even when it’s possible<br />

to create models with predictive<br />

power, they often work over time<br />

horizons that are too short to be useful<br />

in chemicals manufacturing. Predicting<br />

that a part will fail in two days<br />

or two weeks is useful in a truck or<br />

machine tool, but it may not help in a<br />

plant where shutdowns take several<br />

days and maintenance teams require<br />

months to plan interventions and<br />

source spare parts.<br />

• TOO LITTLE IMPACT. The impact from<br />

PdM is often low because plants<br />

operate critical assets with a high<br />

degree of redundancy and few single<br />

points of failure. If a pump stops<br />

unexpectedly, operators can often<br />

switch to a backup unit with little impact<br />

on production.<br />

• Too little savings. Finally, a focus<br />

on reducing unplanned downtime<br />

ignores the largest source of throughput<br />

losses in most plants. Shutdowns<br />

for planned maintenance events<br />

cause OEE losses of 5 to 10 percent on<br />

average, twice as much as unplanned<br />

stoppages.<br />

Towards Digital Reliability<br />

Do these challenges mean analytics<br />

provides little or no value in efforts to<br />

improve asset productivity in the chemicals<br />

sector? No. The industry is achieving<br />

considerable success with a range of<br />

digital reliability techniques, many of<br />

which are far cheaper and less complex<br />

to implement than advanced PdM. Take<br />

three prominent examples:<br />

46 maintworld 1/<strong>2020</strong>


Condition Monitoring<br />

Improving condition monitoring<br />

through better remote sensing can cut<br />

mean time-to-repair, significantly reducing<br />

the impact of equipment failures. At<br />

one chemical plant, a few critical pumps<br />

suffered repeated failures. No backups<br />

were available for these units, and the<br />

issue was a significant source of production<br />

losses at the plant.<br />

“We decided we couldn’t wait for the<br />

plant and reliability engineers to identify<br />

the root cause, redefine the pump’s technical<br />

specifications, and then procure<br />

replacements,” the plant’s maintenance<br />

manager told us. “So, we focused on mitigating<br />

the impact of the failures, rather<br />

than avoiding them.”<br />

The plant’s reliability team installed<br />

a handful of new sensors on the pumps<br />

and started to monitor their condition<br />

online in real time, allowing them to detect<br />

imminent failures a few hours before<br />

they occurred. By enabling maintenance<br />

personnel to be ready to intervene, this<br />

intervention reduced the mean time-torepair<br />

on these pumps from 6.5 2 Predictive<br />

maintenance: the wrong solution to<br />

the right problem in chemicals to around<br />

3 hours, cutting OEE losses by almost<br />

half and saving approximately 120,000<br />

US dollars for each failure.<br />

Smarter Capex Decision–<br />

Making<br />

Better data means better investment decisions,<br />

especially when it comes to the<br />

allocation of sustaining capex costs—or<br />

avoiding equipment failure by making<br />

the right, risk-informed capex decisions.<br />

Most chemical companies struggle to<br />

set the right level of sustaining capex,<br />

as they find it difficult to allocate funds<br />

across multiple plants and disparate asset<br />

types.<br />

This problem is ultimately about ensuring<br />

that resources are used to their<br />

maximum potential—which is exactly<br />

the question that zero-based budgeting<br />

(ZBB) has successfully addressed,<br />

through internal disciplines that assess<br />

all spending in terms of return on investment.<br />

The same techniques apply<br />

to capex investments in assets, or “asset<br />

ZBB,” which combines available historical<br />

data with local expertise to assess the<br />

potential impact of either replacing or<br />

not replacing particular equipment. The<br />

new approach allows all equipment renewal<br />

projects to be compared using the<br />

same yardstick: spend efficiency.<br />

In the words of a manager responsible<br />

for running one chemical manufacturer’s<br />

capital-planning process, “We<br />

used to have a formal process to capture<br />

and assess sustaining capex projects, but<br />

we had no clear way to rank-order projects<br />

and always ended up prioritizing those<br />

with immediate, visible impact. Now we<br />

are able to have a fact-based, data-driven<br />

discussion about risk and trade-offs, which<br />

has led us to spend less overall—and to<br />

manage what we do spend more wisely.”<br />

Root-Cause Problem Solving<br />

Better data also means better root-cause<br />

problem solving. That helps companies<br />

to prevent the recurrence of failures, to<br />

improve their failure-modes and-effects<br />

analysis (FMEA) processes, and to optimize<br />

preventative-maintenance plans.<br />

Together, those actions address the critical<br />

aspects of reliability performance, reducing<br />

both the impact of failures and the cost<br />

of preventing them.<br />

At one chemicals plant, for example,<br />

failures in a critical piece of equipment<br />

caused operators to activate an emergency<br />

shutdown three times in as many months.<br />

These shutdowns were inconvenient<br />

enough—but when the site team attempted<br />

to restart the plant, they found that the<br />

abrupt shutdown of the unit led to an accumulation<br />

of solids in key vessels and pipes.<br />

Fixing that problem led to lengthy delays<br />

in start-up and significant losses in output.<br />

To address the issue, the company applied<br />

a combination of traditional rootcause<br />

problem solving and smart analytical<br />

techniques. Analysis of process data helped<br />

them understand how and why solids were<br />

accumulating under emergency shutdown<br />

conditions. The issue was fixed with a<br />

combination of enhanced monitoring and<br />

changes to preventative maintenance plans.<br />

But the data driven insights also allowed<br />

the plant to revise its emergency shutdown<br />

procedures to stop the plant safely without<br />

causing the solids problem. That change reduced<br />

start-up time after any kind of emergency<br />

shutdown by 90 percent.<br />

The potential for digital reliability extends<br />

far beyond predictive maintenance.<br />

And for chemicals companies, we believe<br />

that these other digital approaches are<br />

both easier to implement and offer greater<br />

value. The highly instrumented nature of<br />

most chemical production facilities means<br />

many companies already have a rich, and<br />

largely untapped, source of data to support<br />

digital reliability efforts. For those plants<br />

that still don’t, then it’s time to “sensor<br />

up”: better data is the vital first step on the<br />

digital-reliability journey.<br />

1/<strong>2020</strong> maintworld 47


Industrial AI in Maintenance:<br />

False Hopes or Real<br />


Artificial intelligence (AI) is an umbrella term for a set of technologies in which<br />

computer systems are programmed to exhibit complex behaviour in challenging<br />

environments. AI is regarded as the major force driving innovation today.<br />

Authors: UDAY KUMAR, DIEGO GALAR and RAMIN KARIM, Luleå University of Technology<br />

FROM AN INDUSTRIAL point of view, AI technologies should be<br />

understood as methods and procedures that enable technical<br />

systems to perceive their environments through context and<br />

situation awareness. They are able to process what they have<br />

monitored and modelled, solve certain problems, find novel<br />

solutions never found by humans, make decisions, and learn<br />

from experience to be better able to manage the processes and<br />

tasks put under AI supervision, Figure 1.<br />

Machine learning (ML) is one area of artificial intelligence<br />

used by industry. Machines need data to learn, either large<br />

quantities of data for one-time analytical purposes, or streams<br />

of data from which learning is continuously taking place. Based<br />

on acquired data either on line or off line, machine learning<br />

can reduce complexity and detect events or patterns, make<br />

predictions, or enable actions to be taken without explicit programming<br />

in the form of the usual ‘if-then’ routines or without<br />

classic automation and control engineering, Figure 2.<br />

Figure 2:<br />

Roadmap<br />

from<br />

traditional<br />

automated<br />

process to<br />

Industrial AI<br />

Figure 1: Solution and knowledge extraction form asset data<br />

48 maintworld 1/<strong>2020</strong>


AI technologies are expected to increase the efficiency<br />

and effectiveness of industrial processes. The primary goals<br />

are to reduce costs, save time, improve quality, and enhance<br />

the robustness of industrial processes. However, AI is not as<br />

well-used in industry as we might expect, given its potential.<br />

Enormous changes and high costs are needed to integrate AI<br />

applications into corporate structures and along the entire<br />

value-added chain. At this point, AI applications tend to be<br />

found in the areas of robotics, knowledge management, quality<br />

control, and maintenance analytics shifting from traditional<br />

approaches to predictive ones.<br />

A good field for AI in maintenance in industrial environments<br />

is the analysis and interpretation of sensor data, distributed<br />

throughout equipment and facilities. The Internet<br />

of Things (IoT), i.e. distributed data-suppliers and data-users<br />

capable of communicating with each other, is the basis for this<br />

use of AI. IoT acquires the data after pre-processing, records<br />

the status of all different aspects of the machines, and performs<br />

actions in process workflows on the basis of its analysis. Its<br />

central purpose is to identify correlations that are not obvious<br />

to humans to enable predictive maintenance (Figure 3), for example,<br />

when complex interrelated mechanical setting parameters<br />

have to be adjusted in response to fluctuating conditions<br />

in the environment to avoid compromising the asset’s health.<br />

Figure 3:<br />

Predictive<br />

maintenance<br />

supported<br />

by AI<br />



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Industrial AI’s capacity to analyze very large amounts of<br />

high-dimensional data can change the current maintenance<br />

paradigm and shift from preventive maintenance systems to<br />

new levels. The key challenge, however, is operationalizing predictive<br />

maintenance, and this is much more than connecting<br />

assets to an AI platform, streaming data, and analyzing those<br />

data. By integrating conventional data such as vibration, current<br />

or temperature with unconventional additional data, such<br />

as audio and image data, including relatively cheap transducers<br />

such as microphones and cameras, Industrial AI can enhance<br />

or even replace more traditional methods. AI’s ability to predict<br />

failures and allow planned interventions can be used to<br />

reduce downtime and operating costs while improving production<br />

yield. For example, AI can extend the life of an asset beyond<br />

what is possible using traditional analytics techniques by<br />

combining data information from designer and manufacturer,<br />

maintenance history, and Internet of Things (IoT) sensor data<br />

from end users, such as anomaly detection in engine-vibration<br />

data, images and video of engine condition. This information<br />

fusion during the lifecycle of the asset is called product lifecycle<br />

management (PLM).<br />

Explainable AI in Maintenance<br />

Advances in AI for maintenance analytics are often tied to<br />

advances in statistical techniques. These tend to be extremely<br />

complex, leveraging vast amounts of data and complex algorithms<br />

to identify patterns and make predictions. This<br />

complexity, coupled with the statistical nature of the relationships<br />

between input data that the asset provides, makes them<br />

difficult to understand, even for expert users, including the<br />

system developers, Figure 4. This makes explainability a major<br />

concern.<br />

Figure 4: Engineers and data scientist must co-create the AI<br />

solution for maintenance together<br />

While increasing the explainability of AI systems can be<br />

beneficial for many reasons, there are challenges in implementing<br />

explainable AI. Different users require different<br />

forms of explanation in different contexts, and different contexts<br />

give rise to different needs. To understand how an AI<br />

system works in the maintenance domain, users might wish to<br />

know which data the system is using, the provenance of those<br />

data, and why they were selected; how the model and prediction<br />

work, and which factors influence a maintenance decision;<br />

and why a particular output is obtained. To understand what<br />

type of explanation is necessary, careful stakeholder engage-<br />

ment and well-thought-out system design are both necessary<br />

as can be seen in figure 5.<br />

Figure 5: Architecture of explainable AI systems for Maintenance<br />

Decisions<br />

There are various approaches to creating interpretable systems.<br />

Some AI is interpretable by design; these systems tend to<br />

be kept relatively simple. An issue with them is that they cannot<br />

get as much customization from vast amounts of data as<br />

more complex techniques, such as deep learning. This creates a<br />

performance-accuracy trade-off in some settings, and the systems<br />

might not be desirable for those applications where high<br />

accuracy is prized. In other words, maintainers must accept<br />

more black boxes.<br />

In some AI systems – especially those using personal data or<br />

those where proprietary information is at stake – the demand<br />

for explainability may interact with concerns about privacy. In<br />

areas such as healthcare and finance, for example, an AI system<br />

might be analyzing sensitive personal data to make a decision<br />

or recommendation. In determining the type of explainability<br />

that is desirable in these cases, organizations using AI will<br />

need to take into account the extent to which different forms<br />

of transparency might result in the release of sensitive insights<br />

about individuals or expose vulnerable groups to harm.<br />

In the area of maintenance, when the AI recommends a<br />

maintenance decision, decision makers need to understand<br />

the underlying reason. Maintenance analytics developers need<br />

to understand what fault features in the input data are guiding<br />

the algorithm before accepting auto-generated diagnosis<br />

reports, and the maintenance engineer needs to understand<br />

which abnormal phenomena are captured by the inference algorithm<br />

before following the repair recommendations.<br />

One of the proposed benefits of increasing the explainability<br />

of AI systems is increased trust in the system. If maintainers<br />

understand what led to an AI-generated decision or recommendation,<br />

they will be more confident in its outputs. But the<br />

link between explanations and trust is complex. If a system<br />

produces convincing but misleading explanations, users might<br />

develop a false sense of confidence or understanding. They<br />

might have too much confidence in the effectiveness or safety<br />

of systems, without such confidence being justified. Explanations<br />

might help increase trust in the short term, but they do<br />

not necessarily help create systems that generate trustworthy<br />

outputs or ensure that those deploying the system make trustworthy<br />

claims about its capabilities.<br />


Galar, Diego, Pasquale Daponte, and Uday Kumar. Handbook of Industry 4.0 and SMART Systems. CRC Press, 2019.<br />

Galar, Diego. Artificial intelligence tools: decision support systems in condition monitoring and diagnosis. Crc Press, 2015.<br />

Galar, Diego, Uday Kumar, and Dammika Seneviratne. Robots, Drones, UAVs and UGVs for Operation and Maintenance. CRC Press, <strong>2020</strong>.<br />

50 maintworld 1/<strong>2020</strong>





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