Maintworld Magazine 2/2021

- maintenance & asset management

- maintenance & asset management


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2/<strong>2021</strong> www.maintworld.com<br />

maintenance & asset management<br />

30 Years<br />

of Adash p 6<br />


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When the World<br />

Requires Realism<br />


BINAR, article, or ad under the<br />

headline “Digitisation”. You<br />

participate in the event and<br />

get to know the subject enthusiastically.<br />

You paint in your<br />

mind incredible ideas on how to<br />

grow your business. In the end,<br />

however, you press your gaze<br />

to the ground and sigh deeply,<br />

because truly genuine solutions<br />

did not exist. There is still no<br />

moon in the sky -moment, but<br />

instead “let’s go brainstorming<br />

together”.<br />

There has been talk of digitisation ongoing for at least the last ten years, and<br />

various service solutions or products have been developed. In terms of maintenance,<br />

there have been discussions on analytics and simulation, artificial intelligence,<br />

machine learning, mobile solutions, and virtual and augmented reality.<br />

As a result, fault prediction, “pre-chewed” data with decision proposals (read<br />

artificial intelligence) and ready-made maintenance instructions for smart<br />

glasses have been promised. For the most part, digitalisation has been described<br />

as a “Superman,” but so far it has shown up more in its day job.<br />

In spring 2020, Covid-19 came and changed everything. Partly for an indefinite<br />

period of time, partly permanently. Earlier digitalisation was partly a<br />

choice, now it is forced. We are forced to find solutions and products that enable<br />

us to operate in changed circumstances. Solutions that can be implemented<br />

now, not in a five years’ time. Suddenly, digitalisation doesn’t have to be an allencompassing<br />

miracle from the planet Krypton. An honest and reliable engineer<br />

is enough. And here, I think, lies the whole thing.<br />

Ongoing digitisation discussion could be described as a 100-meter run. In<br />

the debate there has been a desire to run 100 meters in less than 10 seconds.<br />

This is possible, but the starting level has been at 13 seconds. Thus, we have to<br />

pull on our training clothes and humble ourselves to work towards the 10 second<br />

goal. It is good to know in which direction you are going, and there must be<br />

ambition. Personally, however, I also want to get a real picture and understanding<br />

of the present without the need to take off the mask.<br />

For example, would real-time and visualised data, online training spiced up<br />

with virtual reality exercises, and good mobile solutions be the realism that already<br />

provides significant benefits?<br />

4 maintworld 2/<strong>2021</strong><br />

Erkki Mäkelä<br />

Business Manager, Kiwa Impact | Digital Services at Kiwa<br />

36<br />

The amount of information<br />

available and its use for<br />

analytics is growing exponentially,<br />

making it more<br />

important than ever to base<br />

decisions on accurate and<br />

reliable data.

IN THIS ISSUE 2/<strong>2021</strong><br />

10<br />

The<br />

4th industrial revolution,<br />

IoT and predictive analytics<br />

offer unprecedented<br />

opportunities in the field of<br />

maintenance, reliability and<br />

condition monitoring.<br />

=<br />

30<br />

What<br />

do monkey wrenches<br />

and computer algorithms have<br />

in common? Surprisingly,<br />

wrenches and computer<br />

algorithms are both invented<br />

in the same year 1840.<br />

6<br />

30 Years of Adash<br />

22<br />

20 The Heat is On 36<br />

Asset Performance Awards Night <strong>2021</strong><br />

10<br />

Keep It Simple, Keep It Running:<br />

14<br />

24<br />

ICONICS Asset-based Management<br />

26<br />

and FDD Save Time and Expense<br />

Ultrasound and Vibration analysis: two<br />

18<br />

key elements of predictive maintenance<br />

30<br />

Streamlining Processes:<br />

why should we?<br />

SDT Proudly Announces Vigilant<br />

Reliability and Maintenance<br />

Management (RMM) - The frontline<br />

of maintenance<br />

From Wrench to Algorithm<br />

Measurement Traceability for the<br />

Controlled Environment<br />

38<br />

44<br />

48<br />

05-Steps to Develop Drilling<br />

Organization Asset Integrity<br />

Management Program<br />

Infrastructures Physical Assets High<br />

Performance Achievement based on<br />

Reliability and Maintenance Program,<br />

A.I and Asset Integrity Management<br />

Monetizing Data in Maintenance:<br />

Data-driven Spare Parts Managements<br />

(part 3)<br />

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

Väritehtaankatu 8, 4. kerros, 01300 Vantaa, tel. +358 20 6100, www.omnipress.fi Editor-in-chief Nina Garlo-Melkas tel. +358 50 36 46 491,<br />

nina.garlo@media.fi, Advertisements Kai Portman, Sales Director, tel. +358 358 44 763 2573, ads@maintworld.com Layout Menu Meedia,<br />

www.menuk.ee Subscriptions and Change of Address members toimisto@kunnossapito.fi, non-members tilaajapalvelu@media.fi<br />

Printed by Reusner, www.reusner.ee Frequency 4 issues per year, ISSN L 1798-7024, ISSN 1798-7024 (print), ISSN 1799-8670 (online).<br />

2/<strong>2021</strong> maintworld 5


Adash portable vibration<br />

analysis devices nowadays.<br />

30 Years of Adash<br />

The Adash Company, a producer of vibration analysis instruments and software<br />

is celebrating its 30-year anniversary this year. Though this is an uncommon<br />

article here in <strong>Maintworld</strong>, we have decided to online interview Mr. Adam Bojko<br />

and Mr. Radomir Sglunda who are founders and owners of the Adash Company.<br />

We would like to know more about their successful worldwide business and<br />

basically why and how they started. How is the current market situation and<br />

how do they see a future of machine condition monitoring?<br />

MAINTWORLD: My first question is<br />

probably no surprise. What brought<br />

you two together and what lead you to<br />

a decision to start your own company<br />

concentrating on industrial maintenance<br />

and predictive maintenance in<br />

particular?<br />

RADOMIR: We met at the Coal Research<br />

Institute here in Ostrava in the<br />

6 maintworld 2/<strong>2021</strong><br />

mid 80s. Adash is based here in Ostrava<br />

too by the way. We were in the<br />

seismic measurements department.<br />

That was our first touch with a vibration<br />

analyser. Nobody knew in the beginning<br />

how to operate the B&K 2034<br />

vibration analyser, which was bought<br />

by the Research Institute for our<br />

measuring purposes. It was no surprise<br />

that all manuals where in Eng-<br />

lish only and we needed to study a lot<br />

all the new terms for us. We found out<br />

how to set up correct time waveform,<br />

FFT and other types of measurement<br />

for our seismic measurement purposes.<br />

But why did the manual keep<br />

talking about some "rotating machinery<br />

analysis"? This area was totally<br />

unknown to us, and we were curious<br />

to know more about it. We managed


to get more materials about this<br />

method of predictive maintenance<br />

and started to offer our services to<br />

surrounding factories.<br />

MAINTWORLD: I presume that you<br />

were using competitors’ devices<br />

for your jobs in the beginning. But<br />

what was actually your first product<br />

under the Adash name?<br />

ADAM: We were young with not too<br />

much in the way of resources. We<br />

literally had just our PC. Therefore,<br />

there was only one way for<br />

us to start; to do some programming.<br />

Our first Adash product was<br />

software for Modal Analysis. Later<br />

on we got in touch with TEC from<br />

Knoxville and we made Operating<br />

Deflection Shapes at their instigation.<br />

However, this was specialized<br />

software for a limited amount of<br />

vibration experts. And we were<br />

hungry to catch a bigger audience.<br />

MAINTWORLD: Where did you see<br />

these bigger opportunities?<br />

The first Adash vibration analyser<br />

A4001 from 1995<br />

The first generation (1998) of the Adash<br />

bestseller. The legendary A4900 Vibrio M<br />

vibration data collector. Thousands of pieces<br />

and counting have been sold worldwide.<br />

ADAM: We wanted to attract the<br />

general public. Basically, guys in<br />

the field. People involved in dayto-day<br />

data collection. Adash was<br />

actually the first in the world to<br />

write route measurement management<br />

software DDS which was<br />

running under Windows 3.1 OS. We<br />

were selling it along with competitor’s<br />

data collectors and things finally<br />

started to move. We even sold<br />

tens of copies to our German rival<br />

Pruftechnik in the past when they<br />

did not have their own Windowsbased<br />

software yet.<br />

MAINTWORLD: So why did you also<br />

start hardware production if you<br />

were successfully selling your software<br />

with competitors’ hardware?<br />

RADOMIR: That is a simple answer.<br />

Competitors closed their hardware<br />

communication protocols. Therefore,<br />

our DDS software could not<br />

"talk" with their data collectors<br />

anymore.<br />

MAINTWORLD: Understood. I guess<br />

you started with a simple vibration<br />

meter, correct?<br />

ADAM: Actually no. Our first device<br />

in 1995 was a vibration analyser,<br />

data collector. We hired our<br />

first employee whose hobby was<br />

electronics, and he became our<br />

hardware development engineer.<br />

The vibration analyser A4001 was<br />

presented at the biggest Czech<br />

engineering fair in Brno that year.<br />

I remember the first version at the<br />

fair was not 100% ready for market.<br />

The batteries there drained in<br />

about 10 minutes, so we needed to<br />

be very quick while presenting to<br />

potential clients ☺. This battery<br />

Adam Bojko and Radomir Sglunda in late 80's.<br />

2/<strong>2021</strong> maintworld 7


Adash online vibration monitoring<br />

systems nowadays.<br />

The first software in the world for route measurement<br />

management DDS running under Windows OS from 1993.<br />

The first Adash<br />

online vibration<br />

monitoring<br />

system A3600<br />

from 1996<br />

issue was of course fixed right after<br />

the show.<br />

MAINTWORLD: It seems there has been<br />

huge progress during the years as you<br />

have a wide range of portable and online<br />

monitoring equipment in your<br />

portfolio nowadays. How do you fight<br />

against competition and what is the<br />

current situation in your business area?<br />

RADOMIR: First of all we managed to<br />

find distributors in over 90 countries<br />

during the years. Competition will<br />

always be here which keeps us in pace<br />

to bring new things to clients. We are<br />

developing our products according to<br />

the end users’ requirements actually.<br />

We are listening to their suggestions<br />

8 maintworld 2/<strong>2021</strong><br />

of what should be improved or what<br />

features should be added to our software<br />

or hardware. Technical support<br />

from our distributors and from us is<br />

quite quick I think. We have unique<br />

measurement features and also, we are<br />

competitive price wise. Last but not<br />

least we offer 5 years warranty on our<br />

products.<br />

Our big competitors have been acquired<br />

by huge corporations during<br />

last few years. We think it will slow<br />

down their development in the future<br />

as they have lost independence.<br />

MAINTWORLD: Talking about the future.<br />

How do you see vibration condition<br />

monitoring in the future? What<br />

can we look forward to in Adash?<br />

ADAM: Despite concentrating now on<br />

our portable devices, I think factories<br />

will be turning more to 24/7 online<br />

monitoring systems. But we believe<br />

there will be still a group of vibration<br />

enthusiasts who will appreciate<br />

our portable systems. We also hear a<br />

lot these days about Cloud software,<br />

Wireless solution... We are currently<br />

working on Cloud-based software as it<br />

is a demand from clients to take a look<br />

at data from any part of the world and<br />

to look at the data via a mobile app. We<br />

are ready for Wireless data transmission<br />

to our devices. However, we have<br />

not found a reliable wireless accelerometer<br />

producer on the market so far.<br />

See more on www.adash.com


WIM<br />


Director of BEMAS,<br />

Wim Vancauwenberge (BEMAS)<br />

about Asset Performance 4.0<br />

Conference & Exhibition<br />

"Opportunity to give assets vitamin D in a targeted way”<br />

On 26, 27 and 28 October <strong>2021</strong>, BEMAS (the Belgian Maintenance Association)<br />

will organise Asset Performance 4.0, a hybrid conference and exhibition in Antwerp.<br />

The focus lies on the digitalization of maintenance; reliability and all other<br />

disciplines that influence the performance of assets.<br />

10 maintworld 2/<strong>2021</strong>



MAS, explains the context of the initiative:<br />

"Our world is in the middle of an extremely<br />

exciting phase of continuous digitalization.<br />

Sensors are becoming cheaper, machines and<br />

installations are getting a digital upgrade, data<br />

is being unlocked via the Internet of Things.<br />

The good old creed 'to measure is to know' is<br />

becoming more important than ever! Thanks<br />

to digitalization, this 'measuring' is done on<br />

an increasingly large scale, more accurately,<br />

more thoroughly ánd at a lower cost.<br />

The next step after measuring, the “knowing”,<br />

has also evolved greatly in recent years.<br />

Machine Learning and Artificial Intelligence<br />

allow sensor measurements and other data<br />

sources to be handled very differently. Thanks<br />

to smart algorithms, all data can be combined<br />

to detect deviations at an earlier stage, to predict<br />

what might go wrong and even to propose<br />

the necessary measures to predict problems.<br />

This may concern quality issues, abnormal<br />

energy consumption, and of course technical<br />

malfunctions. Digitization thus helps to take<br />

reliability and maintenance to a higher level.<br />

Vitamin D for Data<br />

Capturing and processing data is one thing.<br />

But the big challenge for an organisation is<br />

what you DO with it. The AP 4.0 conference<br />

will therefore also bring new insights and inspiring<br />

examples of how concrete benefits can<br />

be gained from the advanced digitization of the<br />

production process and asset management.<br />

"We see that organisations often already dispose<br />

of a lot of data. It is therefore especially<br />

important to set up those data and data flows<br />

correctly and to link them to intelligent algorithms.<br />

This allows all business processes to<br />

be boosted with the vitamin D of Data. We see<br />

that more and more organisations are combining<br />

existing good practices with new data-driven<br />

insights in order to create additional added<br />

value with their assets."<br />

Hybrid Conference<br />

& Exhibition<br />

"Since its launch in September 2020, the Asset<br />

Performance 4.0 platform has become a<br />

digital source of inspiration where more than<br />

900 users can find information and inspiration<br />

on digitization in asset intensive organisations.<br />

At the end of October <strong>2021</strong>, we invite<br />

everyone with an interest in this topic to the<br />

FMCCA in Antwerp. Those who participate<br />

on site can network and have in-depth discussions<br />

with the guest speakers and colleagues.<br />

However, it is also possible to follow the presentations<br />

online. Both asset owners and experts<br />

bring inspiring insights and share their<br />

experience with the new technologies. We<br />

also organise a hackathon, where companies<br />

can present their solution to the case live on<br />

stage. At the exhibition (also open for visitors<br />

who aren’t attending the conference), specialised<br />

companies present their best practices<br />

and digital solutions in maintenance, reliability<br />

and asset management. During the Asset<br />

Performance Awards, companies can win a<br />

prize with a case about a (digital) improvement<br />

project. Of course everything will be<br />

organised in accordance with the COVID-19<br />

measures in force at the time.<br />

"We are strongly committed to interaction<br />

and the exchange of experience. AP 4.0<br />

will be an inevitable appointment for anyone<br />

who wants to gain new insights into the current<br />

digitis not only limited to the conference<br />

on 26, 27 and 28 October, but will also<br />

continue to offer weekly webinars via the AP<br />

platform. All presentations made during the<br />

conference will remain available digitally.<br />

Participating therefore means keeping up to<br />

date.", concludes Wim Vancauwenberge.<br />


Register now and you even get free full access to all recordings of the 2020 edition!<br />

2/<strong>2021</strong> maintworld 11


Asset Performance Awards Night <strong>2021</strong><br />

ON THE 27TH OF OCTOBER, the first day<br />

of the Asset Performance Conference &<br />

Exhibition, BEMAS (the Belgian Maintenance<br />

Organisation) organizes the AP<br />

Awards Night. The goal is to highlight<br />

achievements in maintenance and asset<br />

management. Asset owners and service<br />

providers can participate in three categories:<br />

• Asset Performance 4.0 Award<br />

• Best Improvement in Maintenance<br />

and Asset Management<br />

• Technical Team of the Year<br />

After the presentation of the cases, Peter<br />

Hinssen will kick off the Awards Night<br />

with a keynote on “The day after tomorrow<br />

in Asset performance”. After the<br />

winners’ announcement, you can enjoy<br />

the rest of the evening with a luxurious<br />

walking dinner and an opportunity to<br />

network at the exhibition.<br />


09h00 : Reception<br />

11h00 : 3 cases ‘Best Improvement in<br />

Maintenance & Asset<br />

Management‘<br />

14h00 : 3 cases ‘Asset Performance<br />

Award 4.0‘<br />

16h00 : 3 cases ‘Technical Team<br />

of the Year‘<br />

17h30 : Reception<br />

18h30 : Asset Performance Awards<br />

Night with keynote by<br />

Peter Hinssen<br />

19h15 : Announcement of the winners<br />

20h00 : Walking dinner and networking<br />

at the exhibition floor<br />

Sign up now to be present at the<br />

Asset Performance 4.0 Awards Night<br />

(in Antwerp or online!) via<br />

www.assetperformance.eu/<strong>2021</strong> !<br />

You can still participate<br />

as a contestant!<br />

Do you have an inspiring case in<br />

one of the three categories? Sign up<br />

before July 15th, and get a chance to:<br />

• Show that you are proud of the<br />

achievements of your organisation<br />

and the employees in<br />

your team.<br />

• Increase the image of your<br />

technical department and/or<br />

your team and company.<br />

• Give a nice incentive to your<br />

technical team<br />

• Win a fantastic prize and put<br />

your company in the spotlight<br />

for one day long.<br />

Get more info & register via<br />

www.assetperformanceawards.eu<br />

Who should attend the Conference & Exhibition?<br />

ASSET PERFORMANCE 4.0 provides a comprehensive and attractive<br />

agenda of learning opportunities for anyone with<br />

a keen interest in increasing asset performance. The Asset<br />

Performance 4.0 initiative offers essential insights on how to<br />

increase Reliability, Production Output and Quality by using<br />

smart solutions and new technologies without omitting the essential<br />

basics and best practices.<br />

We address both technical and managerial practitioners<br />

active in asset intensive industries across Europe and<br />

throughout the world. The audience includes Operations<br />

& Maintenance Managers, Plant Managers, Production<br />

Managers, CTO’s, Asset Managers, Maintenance & Reliability<br />

Engineers, Service Managers, Quality Managers<br />

and of course Digitisation Managers, Industry 4.0 Program<br />

Managers, AI & Data Engineers and IIoT specialists.<br />

12 maintworld 2/<strong>2021</strong>


Each OnTrak is capable<br />

of 16 sensors. Easily<br />

scale OnTrak systems<br />

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sensors to one<br />

central dashboard<br />



Remote and Real Time<br />

Bearing Monitoring and Lubrication<br />


(Ethernet, wifi or cellular)<br />



Dispense lubricant with<br />

precision only when<br />

needed from up to<br />

16 single point<br />

lubrication devices<br />

The OnTrak SmartLube is a unique remote bearing monitoring<br />

and lubrication system. Designed to monitor and lubricate<br />

bearings remotely. With remote condition-based lubrication<br />

you can greatly reduce bearing failures.<br />

System uses ultrasonic<br />

sensors: identify<br />

bearing issues beyond<br />

lubrication at the<br />

earliest possible point<br />

Lubricate bearings<br />

remotely with a push<br />

of a button, using<br />

always the right grease<br />

and the right amount<br />


Viewable on any<br />

network connected<br />

device; pc, laptop,<br />

tablet, phone using<br />

a standard browser<br />


Built-in events system,<br />

which is configurable,<br />

and has the ability to<br />

display, email and text<br />

any alerts the system has<br />

All data accessible anytime,<br />

anywhere, via user-friendly<br />

dashboards<br />

Easy to install, affordable<br />

and scalable<br />




+44 (0) 7930 352 188 | chrish@uesystems.com<br />

System includes single<br />

point lubricators: no<br />

more lubrication issues!<br />

Integrates with existing<br />

databases and CMMS


Keep It Simple,<br />

Keep It Running<br />

ICONICS Asset-based Management<br />

and FDD Save Time and Expense<br />

Simplicity can often be its own reward and that’s especially true when it comes<br />

to control and monitoring systems for asset-based management and maintenance.<br />

Undoubtedly, in this digital age, there are multiple possible choices for keeping track<br />

of an organization’s connected assets as well as for maintenance operations.<br />

14 maintworld 2/<strong>2021</strong>



Senior Director of<br />

Global Marketing,<br />

ICONICS,<br />

melissa@iconics.com<br />

FOR THOSE COMPANIES considering a<br />

change, or assessing new solutions, it<br />

might be helpful to understand their<br />

many possible options. Some only add<br />

needless complexity and more work<br />

than necessary, while others accomplish<br />

the goals of saving time, making<br />

larger systems more easily manageable,<br />

and helping organizations to<br />

become more proactive with their<br />

maintenance concerns.<br />

ICONICS (https://iconics.com), a<br />

group company of Mitsubishi Electric<br />

Corporation, has created a system<br />

incorporating an easy-to-maintain,<br />

tree-based asset management structure<br />

that allows for data analysis<br />

add-ons, such as for fault detection<br />

and diagnostics (FDD). The Foxborough,<br />

Massachusetts-headquartered<br />

company, now celebrating its 35th anniversary,<br />

is a global automation software<br />

provider of advanced industry<br />

4.0, web-enabled, OPC UA- and<br />

BACnet-certified visualization, analytics,<br />

and mobile software solutions for<br />

any energy, manufacturing, industrial,<br />

or building automation application.<br />

Asset-based Management<br />

for Easier Control and<br />

Monitoring<br />

ICONICS created its AssetWorX<br />

technology, found within its<br />


building automation suite and configured<br />

via its Workbench management<br />

environment, to make it easier<br />

for organizations to monitor and<br />

control digitally connected assets.<br />

It is an ISA-95-compliant solution<br />

that provides an architectural layer<br />

that greatly reduces engineering time<br />

while improving operator consistency<br />

and offering intuitive navigation. It<br />

enables the system to be engineered<br />

and operated based on an intelligent<br />

asset model configured to represent<br />

your organization.<br />

AssetWorX consists of a tree-like<br />

structure in which users can build<br />

a digital twin of their enterprise by<br />

mapping physical locations and business<br />

units, as well as equipment and<br />

machinery, all in one centralized<br />

system. This asset structure then<br />

provides a functional hierarchy for<br />

navigation and for data roll-ups.<br />

Lower-level equipment combines to<br />

form higher levels in the structure.<br />

Physical locations and areas of responsibility<br />

can be identified in the<br />

hierarchy.<br />

The asset tree provides a way<br />

to organize data sources (e.g., OPC,<br />

BACnet, database, web services, etc.)<br />

and visualizations (e.g., HMIs, charts,<br />

heatmaps, reports, etc.) in a logical<br />

hierarchical structure. For example,<br />

rather than OPC data sources being<br />

organized based on the address space<br />

of the server itself, these data sources<br />

can be organized based on the geographic/physical<br />

locations of the associated<br />

sensors (for example, by site,<br />

building, floor, machine, etc.), providing<br />

an invaluable level of contextualization<br />

for insightful analysis.<br />




2/<strong>2021</strong> maintworld 15


Instead of configuring a separate<br />

system for each application (e.g., data<br />

management, aliasing, alarming, etc.),<br />

data from multiple applications can<br />

be configured into a single tree and<br />

organized in a logical structure that<br />

users define. Behind the scenes,<br />

AssetWorX uses existing configuration<br />

providers and services (as plugins)<br />

to configure existing applications.<br />

AssetWorX comes with multiple<br />

predefined nodes at the highest level<br />

of the structure. These nodes are:<br />

• ASSETS - where users can configure<br />

equipment and house their hierarchical<br />

asset tree.<br />

• EQUIPMENT CLASSES - templates<br />

that can be created for building the<br />

asset tree.<br />

• TREE VIEWS - allow users to better<br />

filter and reorganize items within<br />

tree-based navigation.<br />


users identify preferences for<br />

AssetWorX settings.<br />

Within any enterprise, equipment<br />

can be interconnected according<br />

to its physical location or by the<br />

business units into which they are<br />

organized hierarchically. Users can<br />

easily define any number of those interconnected<br />

relationships using the<br />

asset tree. Each piece of equipment<br />

gets its own node in the asset tree,<br />

along with its own associated properties.<br />

A property might be a variable<br />

data source, a reference to an HMI<br />

graphic, an alarm, or a static value.<br />

It is within these equipment properties<br />

where the connection to additional<br />

possible analytics can be made<br />

to one of ICONICS popular analytical<br />

tools. One such tool, FDDWorX, is<br />

ideally suited for asset management<br />

and maintenance.<br />

Fault Detection and Diagnostics<br />

for a Proactive Maintenance<br />

Strategy<br />

FDDWorX is a predictive building<br />

automation solution that uses<br />

ICONICS’ advanced Fault Detection<br />

and Diagnostics technology. Configured<br />

within AssetWorX via Equipment<br />

or Equipment Class properties,<br />

it incorporates algorithms that<br />

weigh the probability of faults and<br />

advise maintenance personnel, operators,<br />

and management of actions<br />

to prevent equipment failures or<br />

excessive use of energy. When equipment<br />

failures occur, advanced software<br />

technology provides automatic<br />

guidance to a list of causes sorted<br />

by probability, resulting in reduced<br />

downtime and lower costs to diagnose<br />

and repair.<br />

FDDWorX collects equipment<br />

process data using industry-standard<br />

data collection mechanisms.<br />

It can automatically generate fault<br />

notifications and reports. Operators<br />

can use real-time displays to analyze<br />

relevant data such as the status of<br />

equipment operating outside the<br />

parameters of a given fault rule.<br />

The technology also takes into account<br />

several National Institute of<br />

Standards and Technology (NIST)<br />

concepts.<br />

ICONICS’ AssetWorX and<br />

FDDWorX technology makes it easier<br />

to connect digital assets for main-<br />

16 maintworld 2/<strong>2021</strong>


tenance operations, whether building<br />

out new facilities/plants<br />

or adding new equipment.<br />

Multiple instances of the same<br />

equipment can be easily added<br />

and updated through a templatized<br />

Equipment Class. Another<br />

option users can take advantage of,<br />

especially for initial deployment, is<br />

to setup repeatable assets through<br />

ICONICS’ Bulk<br />

Asset Configurator, an optional<br />

utility that automatically instantiates<br />

equipment in AssetWorX based<br />

on Equipment Classes and unique<br />

parameter values for each instance<br />

of equipment.<br />

The Bulk Asset Configurator<br />

makes use of a Microsoft Excel<br />

file that contains AssetWorX path<br />

structures to where equipment will<br />

be instantiated, Equipment Classes<br />

to reference, and values for each parameter<br />

that exists in the Equipment<br />

Class used. An additional sheet within<br />

the Excel file can be used to define<br />

alarms and historical data tag definitions<br />

for each property specified in<br />

the given Equipment Class.<br />

ICONICS aims to provide those<br />

responsible for maintenance with<br />

an easy-to-manage framework for<br />

operations. Asset-based management<br />

coupled with fault detection and<br />

diagnostics technology has been successfully<br />

tried and tested by numerous<br />

customers around the world, resulting<br />

in saved time and related costs.<br />

FREE Trial Download of ICONICS Suite Version 10.97<br />

Visit https://iconics.com/Download-ICONICS-Suite to download a trial copy of the latest version of ICONICS Suite,<br />

containing updated AssetWorX and FDDWorX technologies.<br />

ICONICS Institute - FREE Expert-Led Overviews with No Sign-Up<br />

ICONICS continues to add expert-led training videos to its ICONICS Institute video library, including entire sections on<br />

“Asset Organization” and “Fault Detection & Diagnostics”. Visit https://iconics.com/ICONICS-Institute to see the latest today!<br />

2/<strong>2021</strong> maintworld 17


Ultrasound and Vibration<br />

analysis: two key elements<br />

of predictive maintenance<br />

Vibration analysis has been for many<br />

years the technology of choice for<br />

maintenance professionals to monitor<br />

the condition of rotating assets.<br />

However, in the last years ultrasound<br />

has also emerged as a very popular<br />

technology for condition monitoring.<br />

The question that many are now asking<br />

themselves is: which one is best?<br />

Ultrasound or vibration? In this article<br />

we will focus on the role of ultrasound<br />

as a condition monitoring tool, and why<br />

using vibration and ultrasound together<br />

is the best way to reach excellence in<br />

your maintenance practices.<br />

Why vibration analysis?<br />

Vibration analysis is an incredible tool: it detects and measures<br />

small vibrations and what is causing them, thus allowing<br />

maintenance professionals to detect early failures on rotating<br />

equipment. Furthermore, vibration analysis gives us a very<br />

deep diagnostic and allows us to identify the failure’s root<br />

cause, and thus correct it to avoid further issues in the future.<br />

Plus, there are a great number of vibration sensors and solutions<br />

on the market to choose for, so maintenance teams can<br />

find a solution that is suitable for their needs.<br />

Why Ultrasound?<br />

Ultrasound is considered by many the first line of defence<br />

when it comes to bearing failures, since it can give a very early<br />

warning of a potential problem, even with lubrication issues.<br />

The way ultrasound does that is by monitoring the friction levels<br />

on rotating equipment. The concept is simple: as a bearing<br />

starts to fail, or if it has not been lubricated properly (under or<br />

over lubricated), the friction levels rise. Friction creates ultrasound<br />

emissions that can be picked up by an ultrasonic handheld<br />

device or sensor and translated to low frequency sounds<br />

that the inspector can hear. Ultrasound equipment will also<br />

provide a decibel level – and the higher the decibel, the higher<br />

the friction.<br />

Ultrasound or vibration?<br />

There is no easy answer to this question, but one thing is for<br />

sure: if a maintenance team wants to reach excellence, both<br />

technologies should be used. Ultrasound will provide the<br />

earliest warning of failure and is also very easy to use, since it<br />

relies on simply trending decibel levels. Vibration analysis is<br />

extremely complete and will give maintenance professionals<br />

a deep overview of the issue and the root cause of such issue.<br />

Almost as if ultrasound is the doctor who detects the problem,<br />

and vibration is the health specialist that will diagnose it properly.<br />

We will now talk about a few situations where, in general,<br />

ultrasound can be used instead of vibration analysis.<br />

18 maintworld 2/<strong>2021</strong>


Slow speed bearings<br />

Slow speed bearings are difficult to monitor. Since they rotate<br />

very slowly, it is difficult for vibration sensors to pick up<br />

significant changes in vibration. Even with an ultrasound instrument<br />

it may be difficult to pick up failures if we rely only<br />

on decibel levels, since in extreme slow speed bearing applications<br />

(usually less than 25rpm), the bearing will produce little<br />

to no ultrasonic noise. However, high-end ultrasonic devices<br />

will allow for sound recording: by recording the sound of the<br />

bearing and checking it in a spectrum analysis software, we<br />

can easily find peaks in the sound spectrum amplitude which<br />

indicate a fault in the bearing.<br />

First line of defence, easy to use<br />

For a maintenance professional to properly work with vibration<br />

analysis, significant training and experience are needed.<br />

On the other side, ultrasound has a much quicker learning<br />

curve. And this is because of how the technology works: since it<br />

is monitoring friction levels and translating them to dB values,<br />

we can easily check for potential problems with our rotating<br />

equipment. Once we setup a dB baseline for a bearing, we just<br />

need to trend the dB value overtime. So, if the baseline for a<br />

bearing is 20dB, but the ultrasonic instrument reads 32db, we<br />

already know there is a problem simply by comparing values.<br />

Lubrication<br />

Again, because ultrasound is based on the friction levels, it<br />

is perfectly adequate for bearing lubrication. Is the bearing<br />

lacking lubrication? Then the friction levels will increase,<br />

and we can hear that through the ultrasonic instrument and<br />

see it in the dB levels. If we start lubricating the bearing,<br />

most likely we will see a decrease in the sound intensity and<br />

the dB levels. Did the bearing receive too much lubricant?<br />

Then again, friction levels will increase, and we will know<br />

that using the ultrasonic instrument. Thus, ultrasound is<br />

perfect to avoid under- and over-lubrication issues.<br />

Versatility<br />

While vibration analysis is an extremely powerful tool, its<br />

uses are limited to mechanical equipment. On the other hand,<br />

ultrasound has a wide range of applications which makes it<br />

a very versatile technology. One of the most popular applications<br />

of ultrasound, besides condition monitoring, is energy<br />

savings. Since turbulence also creates ultrasound emissions,<br />

the ultrasonic instruments can easily be used for leak detection<br />

(compressed air and other gases), steam traps inspection and<br />

even for electrical inspections, to detect issues such as corona,<br />

tracking and arcing.<br />

Conclusion<br />

We believe, as many other maintenance professionals nowadays,<br />

that using multiple technologies that complement each<br />

other is the way to go. Therefore, the question is not ultrasound<br />

vs vibration, but instead ultrasound AND vibration and when<br />

we should be using one or the other. Both are very powerful<br />

condition monitoring technologies and, when used properly together,<br />

can really take any maintenance and reliability program<br />

to the excellence level.<br />

2/<strong>2021</strong> maintworld 19



Senior Reliability Engineer, Easy-Laser AB<br />

The heat is on<br />

All rotating machinery are subjected to thermal<br />

exposure. The machines will react depending on<br />

temperature and material. Either by expanding or<br />

shrinking. And that is a fact. Thermal growth is a<br />

serious thing when you think about it.<br />

The machines might expand differently<br />

ALL ROTATING MACHINERY are installed<br />

in trains. Trains means there is a driver,<br />

which is the motor, and driven which can<br />

be the pump, blower, compressor, or any<br />

different process machine. When rotating<br />

machinery is installed, precision shaft<br />

alignment is performed. Shaft alignment<br />

will ensure both shafts (driver and driven)<br />

are collinear. Collinear means that both<br />

rotational centrelines are positioned as if<br />

they were one.<br />

Different situations<br />

When the machines are started the driver<br />

and driven heat up in very different ways.<br />

A compressor in a hot environment will<br />

quickly increase in temperature due to<br />

friction of its internal rotating parts, and<br />

compression of the media will generate<br />

and add more heat. Comparing to the<br />

20 maintworld 2/<strong>2021</strong>


driver, which can be an electrical motor, the<br />

situation is very different. The temperature<br />

will increase to a certain level and then<br />

remain the same. Two machines with two<br />

different behaviours.<br />

So, what happens when one of them<br />

increase its temperature respectively to the<br />

other? It’s simple; the machine will start expanding.<br />

And when the machine expands,<br />

it will grow in all directions and move its rotational<br />

centre out of collinearity and cause<br />

misalignment. But not only misalignment.<br />

Since there is a change in the machine geometry,<br />

pipe strain might also appear adding<br />

more stress to the housing.<br />

Many consequences<br />

There are so many consequences of<br />

thermal growth in rotating equipment.<br />

Misalignment will for example also result<br />

in bent shaft. Bent shaft will result in unproper<br />

distribution of forces in the bearing<br />

which in turn will lead to failure of the<br />

lubrication. Therefore, we must be able to<br />

anticipate thermal growth by using available<br />

information from the OEM, or by<br />

performing the calculation by ourselves.<br />

How do we do that? The key is to identify<br />

how much growth is expected. This<br />








Thermal expansion by material<br />

number must be used when performing the<br />

shaft alignment to “intentionally misalign”<br />

the machines prior to start. Let us use the<br />

compressor as an example again. If we assume<br />

that the compressor will operate at<br />

higher temperature than the motor, when<br />

aligning, we must place the compressor below<br />

the rotational centreline of the motor.<br />

How much will be determined by expected<br />

thermal expansion growth of the material.<br />

Test run<br />

When the machine is aligned considering<br />

the thermal growth, it must run and operate<br />

until it reaches its full operating condition.<br />

Then it must be stopped, and the shaft<br />

alignment must be verified. This is our test<br />

run of the machine to confirm proper and<br />

reliable installation to be able to achieve<br />

full operational life. We want to test before<br />

we go to full production to make sure our<br />

thermal expansion calculation was right.<br />

Think about aircraft maintenance. When<br />

there is an aircraft engine replacement, to<br />

make sure it is operating as it should, the<br />

pilots perform test flights until everything<br />

can be confirmed. And you don’t want to be<br />

on the plane knowing nobody performed<br />

the test run, do you?<br />

Reveal Your Potential<br />

Get a Reliability and Maintenance Assessment<br />

Call us +1 919-847-8764


Streamlining<br />

Processes:<br />

why should we?<br />

Admittedly, writing out uniform work<br />

processes with associated roles and<br />

responsibilities is not exactly a task to<br />

look forward to. It is like cleaning up<br />

the attic, which over the years has become<br />

cluttered with boxes. However, if<br />

you put some effort into this you will<br />

benefit from overview, structure, clarity,<br />

and a much more efficient way of<br />

working. Ready to roll up your sleeves?<br />




content of the functions within maintenance<br />

exactly the same in every department?<br />

And are ‘best practices’ easily<br />

shared between branches and departments?<br />

– Chances are you have to admit that<br />

something could certainly be improved.<br />

We hear this a lot, says Peter Decaigny<br />

of Mainnovation.<br />

– But anyone who can answer the<br />

above questions with ‘yes’ has a good<br />

basis for streamlining the organization<br />

and/or successfully implementing an<br />

IT tool. And this is the foundation for<br />

further improvements in effectiveness<br />

and efficiency.<br />

22 maintworld 2/<strong>2021</strong><br />

Clutter in the Attic<br />

Many Maintenance and Asset Management<br />

organizations have grown organically<br />

and there is nothing wrong with<br />

that. It all makes sense to the people<br />

involved in this growth. Maybe some<br />

choices were a compromise under pressure<br />

from stakeholders at the time.<br />

Sometimes organizations are built<br />

around people and not based on clear<br />

roles and responsibilities. Some IT tools<br />

are configured to mimic the functionality<br />

of old IT tools. And often, in large<br />

companies, the different maintenance<br />

departments or teams work in completely<br />

different ways.<br />

It is like ‘clutter in the attic.’ In the<br />

beginning there was a system, a certain<br />

storage structure, but more stuff was<br />

added and eventually you find you first<br />

have to move other boxes, before you<br />

find the right box. "Efficiency can be<br />

gained. This starts with cleaning up<br />

and drawing up a plan on how to prevent<br />

this cluttering."<br />

Uniform work processes<br />

With a process map you can describe<br />

the future (desired) situation. This<br />

document is a detailed description of all<br />

processes that are important within the<br />

Maintenance and Asset Management<br />

organization.<br />

Uniform work processes can be written<br />

down in two ways: you either start<br />

from scratch with noting down all steps<br />

in a process, or you can use an existing<br />

process map. "Obviously, both ways have<br />

advantages and disadvantages," explains<br />

Decaigny. "With a blank start, everyone<br />

can have their say, which results in<br />

commitment. However, it is very timeconsuming."<br />

By using an existing process<br />

map, there is already a considerable basis<br />

and this is of course a time saver. "But in<br />

this case that basis must be good, obviously.<br />

After all… reorganizing the attic<br />

when the floor is rotten, wouldn't be a<br />

good idea."<br />

Based on best practices<br />

That is why Mainnovation uses the<br />

VDM XL Process Map. This Process Map<br />

is based on proven ‘best practices’ of leading<br />

Maintenance & Asset Management<br />

organizations from various industries.<br />

This method is written down in more than<br />

60 work processes that together form the<br />

VDM XL Process Map. This template is then


critically assessed in various workshops<br />

within your company. What is missing<br />

and what is or is not applicable for this<br />

organization?<br />

– And yes, that works, says Decaigny.<br />

– Process operations are often very<br />

generic and by fine-tuning them we get<br />

a process map that is very customerspecific.<br />

– There are companies that claim<br />

that they have already written maintenance<br />

processes in place, says Decaigny.<br />

– But in most cases they are based<br />

on what the ISO 9.001 manual provides.<br />

However, these process descriptions<br />

are of little value for restructuring an<br />

organization. They are too high level<br />

and do not provide enough guidance for<br />

the next steps.<br />

The next step would be to use the<br />

processes – that describe what needs<br />

to be done – for assigning the roles and<br />

associated responsibilities and KPIs.<br />

And the ultimate result is the practical<br />

work instruction that emerges from<br />

this. This describes the necessary actions<br />

with a focus on how things should<br />

be done.<br />

Roll up your sleeves<br />

This probably sounds like a tremendous<br />

amount of work. Sitting at the<br />

computer for days and writing, evaluating<br />

and writing again. Arguing the<br />

right approach because nobody wants<br />

to change their way of working. You<br />

can of course save this task for a rainy<br />

day, but the clutter in the attic will<br />

pile up ...<br />

And there are several reasons to<br />

roll up your sleeves anyway.<br />

– A uniform process map is a perfect<br />

starting point for ISO certification.<br />

The VDM XL Process Map connects<br />

seamlessly with ISO 55000.<br />

When a multisite company decides<br />

to implement one EAM system for all<br />

locations – which occurs regularly –<br />

the process design can be used as a<br />

blueprint for this (future) common<br />

IT tool.<br />

– Of course, there can still be differences<br />

in how the organization is set up.<br />

When responsibilities are assigned to<br />

a particular role rather than a specific<br />

job, uniform procedures can still be<br />

used. After all, you want the implementation<br />

to take place in a way that<br />

is labelled as most effective and most<br />

efficient. It goes without saying that<br />

this way of working delivers value, Decaigny<br />

explains.<br />

A uniform working method also has<br />

advantages for training (new) employees.<br />

– In short, it pays off. It takes time<br />

and commitment but rolling up your<br />

sleeves now means creating room for<br />

improvement and growth in the future.<br />

Are you interested in the VDM XL Process<br />

Map? The Mainnovation consultants<br />

are happy to help you structure and<br />

implement uniformity in processes, organization,<br />

IT tools and KPIs. Check the<br />

website for more information or send an<br />

email to info@mainnovation.com.<br />

2/<strong>2021</strong> maintworld 23


SDT Proudly Announces Vigilant<br />

SDT Ultrasound Solutions is excited to announce<br />

the release of Vigilant, the newest<br />

addition to its family of permanent condition<br />

monitoring solutions. Vigilant is an 8-channel<br />

online condition monitoring solution that combines<br />

the versatility of ultrasound diagnostics<br />

with the analytics of vibration data. An<br />

additional 4 channels allow inputs for more<br />

conventional machinery information such as<br />

temperature, RPM, and other process data.<br />

VIGILANT is a stand-alone solution. With its own embedded<br />

software contained within the measurement pod, anyone with<br />

network credentials has access to critical asset health via a web<br />

browser. Using standard communications protocols like Ethernet,<br />

OPC, and Modbus TCP, Vigilant’s communication capabilities<br />

makes the sharing of asset data to other information systems easy.<br />

“ There are many applications where using a single condition<br />

monitoring technology to attempt to identify a failure mode doesn’t<br />

net the outcomes needed by reliability planners”, explains Vigilant<br />

product specialist Robert Dent. “ Vigilant combines insights from<br />

two proven technologies to bring conditional data into a common location,<br />

while providing industry standard tools to perform analysis.”<br />

As assets become more heavily guarded for safety protocol, condition<br />

monitoring teams need more creative ways to access collection<br />

points. Vigilant solves the accessibility dilemma by mounting<br />

low-cost, high-quality permanent ultrasound and vibration sensors<br />

to the asset, and then connecting the data directly to the asset owner.<br />

“ SDT Ultrasound Solutions has always provided ways to collect<br />

and combine Ultrasound and Vibration measurements with our<br />

handheld portable systems”, said SDT’s Benoît Degraeve, area sales<br />

manager and Vigilant product specialist in Europe. “ Today, we do the<br />

same thing with Vigilant, in a safe, reliable and permanent way.”<br />

Vigilant is available in two configurations: Mobility and Permanent.<br />

Vigilant Mobility comes packaged in a rugged, environmentally<br />

protected carrying case and is designed to travel with you to fieldlevel<br />

critical assets where temporary 24/7 monitoring is required.<br />

Vigilant Permanent installs and remains on an asset for its life cycle.<br />

Protected in your own enclosure, Permanent requires a 24V power<br />

source and communications connection.<br />

Vigilant manages both Static and Dynamic data, creating an opportunity<br />

to establish long-term trending, analysis, and diagnosis at<br />

the earliest point in the failure curve.<br />

24 maintworld 2/<strong>2021</strong>


Reliability and Maintenance<br />

Management (RMM)<br />

- The frontline of maintenance<br />

Many Reliability and Maintenance improvement initiatives fail to deliver<br />

sustainable (and continuously improving) results in improved safety,<br />

manufacturing throughput, and costs. There are many reasons for this, but the<br />

lack of engaged, visible, and caring plant leadership is the most common of them.<br />


26 maintworld 2/<strong>2021</strong>


TAKING REGULAR WALKS to visit the frontline of maintenance,<br />

or Gemba walks, is an excellent way for a leader to demonstrate<br />

engagement. Such walks take management to the front lines<br />

showing that they are an engaged, visible and caring leader<br />

and also enables them to learn first-hand what improvement<br />

opportunities exist. You are visible and you show that you<br />

care. This is key to successfully improving your maintenance<br />

organizations’ performance and delivering continuously better<br />

results.<br />

Gemba, sometimes spelled Genba, is Japanese for “where<br />

things happen,” or "the real place." It can be anything from an<br />

actual crime scene to the context of an RMM organization,<br />

where planners, front-line leaders, coordinators, engineers,<br />

operators, crafts people cand storeroom employees close to the<br />

manufacturing floor make things happen. They are the ones<br />

who will execute your initiatives, such as preventive maintenance,<br />

work management, stores organization including,<br />

delivery and “kitting” of parts and material, and many more<br />

improvements. If they do not execute your initiatives, then all<br />

you have is a plan.<br />

As leaders, we are often pressed for time, but carving time<br />

to engage with the frontline is crucial for success. Remember -<br />

improving reliability and maintenance performance is “90%”<br />

about people and processes, so it is important that you are visible<br />

and available.<br />

If you organize and schedule frontline walks well, you will<br />

find that it does not have to take much time. You will also find<br />

it interesting and rewarding when you see the appreciation and<br />

improvements.<br />

I suggest the following steps for your frontline walks initiative:<br />

1. Organize and plan<br />

2. Inform<br />

3. Observe and learn<br />

4. Ask questions, meet face to face<br />

5. Act and follow up<br />

1.Organize<br />

Depending on the size of the Maintenance organization, the<br />

plant manager might do a frontline walk once a quarter per<br />

production area. Operations and Maintenance managers<br />

might do the walk together once a month in their respective<br />

areas. Supervisors should do it weekly.<br />

Set up an improvement activity to focus on for each walk.<br />

Do not include things already on your meeting agendas. No<br />

need for duplication.<br />

It is very beneficial if you have done an assessment of improvement<br />

opportunities and a plan on how to close the gap<br />

between how good you are now to how good you can become in<br />

three to five years.<br />

If you have done that and have identified the gaps you need<br />

to close, it will serve as a setup- and focus guide, for each walk.<br />

This will most likely include the following areas to improve<br />

upon.<br />

• Lubrication practices<br />

• Precision maintenance practices<br />

• Basic inspections and predictive technology<br />

• Work management<br />

• Storeroom management<br />

2/<strong>2021</strong> maintworld 27


By taking full advantage of the<br />

purpose of a frontline walk, you<br />

will show that you are engaged,<br />

visible (accessible) and caring.<br />

There can be other areas, but as good maintenance always<br />

requires—the basics must be executed well before you will be<br />

in a position to move towards excellence. Most plants I work<br />

with need to focus on the areas mentioned above.<br />

In a small plant the walk could include several improvement<br />

areas, while it will be necessary to select one or two focus<br />

areas in a bigger plant. Try to limit the “Observe and learn”<br />

and the “Ask questions, meet face to face” to 30 minutes each.<br />

These steps can be done the same day or on different days.<br />

2.Inform<br />

When you organize and plan your frontline walks, it is very<br />

important to share why and how you will do them with those<br />

involved.<br />

If you, as a plant, maintenance or operations manager, have<br />

a habit to be visible on the manufacturing floor and often talk<br />

with the frontline people, the walks will help organize your<br />

visits towards defined and selected improvement areas. For<br />

frontline leaders this is, of course, not much of an issue as<br />

you work closely with your people daily. If you, as a manager<br />

have kept a distance to the frontline organization it may be a<br />

welcome change. If your visibility was limited in the past, your<br />

appearance may be associated with trouble. This will change<br />

when people understand why and what the Gemba walks<br />

mean to the whole organization.<br />

3.Observe and learn<br />

Before you do “Ask questions, meet face to face” walks—visit<br />

the chosen production area you will observe and study the<br />






equipment so you can ask the right questions. The first time you<br />

do this walk preparing questions might take a bit more time.<br />

If you chose lubrication practices in one area you should look at:<br />

• Blown out seals indicating too much grease and poor<br />

seals.<br />

• Use of tools to measure grease volume on grease guns.<br />

• Clean tools and grease points.<br />

• Oil levels in gears, pumps etc.<br />

• Keep oil level indicators clean to see levels and colour of<br />

oil.<br />

• Right oil levels marked.<br />

• Lubricant type marked with a symbol.<br />

• Lubrication stores clean and organized.<br />

• Air breathers/filters installed where needed. Change in<br />

colour?<br />

• Leaks.<br />

• Long grease lines for manual greasing.<br />

28 maintworld 2/<strong>2021</strong>

If you chose Precision maintenance practices in one area you<br />

should look at:<br />

• Jacking bolts installed for precision alignment.<br />

• Jacking bolts backed off and not pushing on alignment<br />

object.<br />

• More than three shims used for alignment.<br />

• Beat marks on equipment feet.<br />

• Vibrating fans and high-speed equipment.<br />

• Filters for mechanical seals and hydraulic fluids.<br />

Other operating practices you should look at include if<br />

your redundant pumps are running equal time per each<br />

schedule and who is responsible for shifting the pumps<br />

according to that schedule. For more tips on what to<br />

look for during your walks, take a look at IDCON’s new<br />

series of Gemba videos at our website or our YouTube<br />

Channel.<br />

4. Ask questions, meet face to face<br />

After “Observe and learn”, you have the material to be<br />

well prepared to meet face to face and ask questions.<br />

Meet with the people in production who are responsible<br />

for various areas like lubrication, for example. Ask<br />

open-ended questions such as: “I saw a lot of grease<br />

pushed out from the drive side bearing of the fan pump,<br />

what can we do about that?” The answer might be: “That<br />

seal was gone a long time ago, so we need to purge out<br />

the water to save the bearing, we have asked to have it<br />

replaced many times. We also have new lubricators who<br />

have not been trained and think the more grease the<br />

better. We have also asked to get grease meters to put<br />

on the grease guns, so we know how much grease each<br />

point gets.”<br />

Other possible questions: “What would you like to<br />

spend more or less time on?”<br />

For the lubrication program, “What is the most important<br />

thing to improve upon?”<br />

The discussion will teach you a lot. But perhaps more<br />

importantly is the motivation you create by being engaged,<br />

visible, and caring as a leader. This goal will only<br />

be reached if you continue these walks.<br />

5. Act and follow up<br />

Be prepared for suspicion. Many people have seen new<br />

improvement initiatives come and go and may not be impressed.<br />

You have to prove this will be a continuous practice.<br />

During the frontline walks it is important to take notes<br />

and pictures. This will take less and less time as it becomes<br />

routine. It is important to inform your people what you decided<br />

to act upon. If you decided to get the equipment lubricators<br />

needed and train all lubricators, for example, then you<br />

might want to change the damaged seals. It is equally important<br />

to share what you decided against, and why.<br />

When well-organized, the five steps of the frontline walks<br />

will take a manager in a big plant about an hour per quarter/<br />

area. For operations and maintenance managers walking<br />

together it might take an hour a month per area, and frontline<br />

leaders much less time per week. I am sure you will find<br />

it worth the time as your Safety, reliability and cost will<br />



From wrench<br />

to algorithm<br />

From “seeing” to “self-optimizing”: the past, present and future of maintenance<br />

What do monkey wrenches and computer algorithms have in common? Surprisingly,<br />

wrenches and computer algorithms are both invented in the same year 1840.<br />

DIRK DE NUTTE, CEO The Grain<br />

THE MONKEY WRENCH was invented<br />

by American knife manufacturer Loring<br />

Coes. On the other side of the ocean<br />

countess Ada Lovelace produced the<br />

very first "computer program" for the<br />

mechanical Analytical Engine (A.E.),<br />

invented by Charles Babbage named the<br />

father of computers, to calculate Bernoulli<br />

numbers.<br />

Ada Lovelace was not only a brilliant<br />

mathematician, she also discovered the<br />

full potential of this engine, developing<br />

the first algorithm[1]for the A.E. and<br />

becoming as such one of the first computer<br />

programmers. And now, some two<br />

centuries later, computers and wrenches<br />

seem to come together in a new era of<br />

maintenance.<br />

Nothing has changed<br />

but everything changes<br />

Maintenance and repair have been<br />

around as long as mankind exists. From<br />

the time-based sharpening of man’s<br />

earliest spears and tools to the maintenance<br />

concepts for modern equipment<br />

and technologies. Over these last couple<br />

of centuries maintenance has made an<br />

30 maintworld 2/<strong>2021</strong><br />

evolution and gradually merged from<br />

a corrective approach known as “break<br />

and fix”, over a preventive or time-based<br />

maintenance towards a predictive or<br />

condition-based maintenance. And<br />

now we are moving into the new era<br />

of an integrated and digitized phase of<br />

prescriptive maintenance, combining algorithms<br />

of computer science with common<br />

maintenance techniques. By using<br />

artificial intelligence (AI), we can now<br />

master all kinds of data like asset health<br />

data, process data, historical maintenance<br />

data and contextual-operational<br />

data from various sources. We get better<br />

and in-depth insights on asset behaviour,<br />

allowing us to act before failure modes<br />

are manifested. With the AI approach we<br />

can now forecast asset failures modes,<br />

and thus optimize not only the asset<br />

performance but the total plant performance<br />

and production cycle.<br />

Even though our machines, industry<br />

and technology changed over time, the<br />

true foundational elements of how assets<br />

fail remain unchanged, i.e. the failure<br />

patterns and the P-F curve. To see what<br />

our future brings, it’s wise to know our<br />

past, and that’s why we’ll make a brief<br />

journey through the history of modern<br />

maintenance.<br />

From the first industrial<br />

revolution until WOII<br />

During this period maintenance gradually<br />

changed from “break and fix” to<br />

“time-based maintenance”. This is the<br />

era of descriptive maintenance where<br />

engineers “see” and “feel” failures.<br />

Maintenance management has its origins<br />

in the manufacturing industry. The<br />

invention of the steam engine, together<br />

with other inventions like telephone and<br />

radio announced the start of the first<br />

industrial revolution in the 18th century.<br />

At the same time a gradual shift from<br />

human labour to machine production<br />

began. The maintenance strategy was<br />

very simple: keep the machine running<br />

until it fails, only fix when broken. This<br />

is also known as corrective maintenance.<br />

Machine breakdowns were tolerated and<br />

considered “normal”.<br />

During the second industrial revolution,<br />

mid-to-late 19th century, electricdriven<br />

machines were invented and


those required a more sophisticated<br />

maintenance approach. Plant engineers<br />

became more “proactive” to maintain<br />

their equipment. They put a time-based<br />

maintenance strategy in place, and<br />

machine parts where replaced at specific<br />

time intervals. Unfortunately, this<br />

maintenance strategy was, and still is,<br />

quite expensive, as machines need to be<br />

shutdown causing production losses, and<br />

wasteful as parts are being changed according<br />

to a strict schedule whether this<br />

is necessary or not.<br />

Machinery at that time was rugged<br />

and “slow running”, instrumentation<br />

and control systems were very basic.<br />

Economy and production requirements<br />

were not as demanding as they are today,<br />

and breakdowns were a less critical issue.<br />

A break and fix approach and/or<br />

time-based maintenance strategy were<br />

adequately enough. At that time machinery<br />

was “over-engineered” and build<br />

very sturdily and inherently reliable.<br />

Post war till late sixties<br />

During this period maintenance matured<br />

from “Planned preventative maintenance”<br />

to the first concepts of “Reliability<br />

Centred Maintenance”. This is<br />

the era of early diagnostic maintenance<br />

where maintenance engineers “investigate”<br />

and “understand” failures.<br />

From the 1950’s onwards, the post<br />

war economy picked-up quite rapidly.<br />

Industries needed to be rebuilt, especially<br />

those of Japan and Germany. International<br />

economy started flourishing,<br />

creating a more competitive marketplace.<br />

Machine downtime was no longer<br />

tolerated and labour cost became an important<br />

factor leading to mechanization<br />

and automation. Machinery became less<br />

sturdy, was built lighter and ran at higher<br />

speeds. Equipment was used more intensively<br />

causing more wear out, more<br />

vulnerable and so less reliable.<br />

Japanese engineers started following<br />

the manufacturer’s instructions. This<br />

was also the period that the first maintenance<br />

associations and societies were<br />

created, and first global networks established.<br />

That trend gave birth to what<br />

we know as “preventive maintenance”<br />

today. Gradually, those associations encouraged<br />

technicians and other specialists<br />

to develop time-based maintenance<br />

schedules: machine lubrication, machine<br />

inspections, reporting any observations<br />

to help prevent machine damages.<br />

Maintenance and inspection checklist<br />

where used but the disadvantages of this<br />

strategy soon became obvious. The fact<br />

that very often machines needed to be<br />

shut down to perform these inspections,<br />

caused production losses. Repetitive and<br />

often boring interventions lead to negative<br />

“human behaviour” such as “PM<br />

creep” (adding or increasing frequency<br />

of PM’s to the program for no failure<br />

mode related reason) and “pencil whipping”<br />

(signing off on work that has not<br />

been done) causing ineffectiveness and<br />

unnecessary costs.<br />

With the arrival of the Boeing 747<br />

in the late sixties, the aircraft industry<br />

needed to improve reliability and therefore<br />

defined a detailed maintenance<br />

strategy reducing the risk of equipment<br />

failures. Risk had become a new driver<br />

for maintenance. This challenged the<br />

current maintenance strategies and<br />

the long-established basic assumption<br />

that the older equipment gets, the more<br />

likely it is to fail. Reliability centred<br />

maintenance (RCM), a new maintenance<br />

strategy approach was developed<br />

by Nowlan and Heap. The term was first<br />

used in public by United Airlines. Shortly<br />

after, the concept was quickly adopted by<br />

other industries.<br />

With time, other industries began<br />

understanding the value of maintenance<br />

and realised that it had a strategic impact<br />

affecting the bottom line. Since then<br />

proactive elements were increasingly integrated<br />

in well-balanced maintenance<br />

strategies, giving rise to other methodologies<br />

such as risk-based inspections<br />

(RBI), overhaul, etc.<br />

While predictive maintenance, focussing<br />

on eliminating failure modes, had<br />

become common practice in the aviation<br />

industry, the more conservative industry<br />

was lagging behind and still relying on<br />

time-based maintenance.<br />

From late 60’s till late 80’s<br />

During this period the notion of “maintenance<br />

service” and “condition-based<br />

monitoring” emerged. This is the era that<br />

diagnostic maintenance made its first<br />

steps into the broader industry.<br />

Between the 1960’s and 80’s, maintenance<br />

was considered a side activity<br />

and seen as of minor importance, only<br />

necessary when a breakdown occurred.<br />

The maintenance department’s scope was<br />

restricted and mainly limited to electrical,<br />

2/<strong>2021</strong> maintworld 31


mechanical repairs or greasing work. The<br />

notions of prediction or prevention were<br />

not at all integrated, and maintenance often<br />

suffered from a bad image.<br />

The industrial world, as well as the implications<br />

of failures, were however very<br />

different from the ones we know today.<br />

At that time, industry was burgeoning,<br />

consequences on production lines weren’t<br />

the same at all. Production shutdowns disrupted<br />

the production but weren’t leading<br />

to huge losses like today. During this period<br />

companies became progressively aware<br />

of the impact and importance of safety.<br />

Due to industry gearing to mass production,<br />

machines had become fast running,<br />

more advanced, more complex and were<br />

driven to their limits, causing higher risk.<br />

Hence, maintenance gained importance.<br />

The first maintenance procedures<br />

were developed, reducing working accidents<br />

and avoiding critical breakdowns.<br />

Interesting to note is that the main driver<br />

was human integrity (safety) rather than<br />

economic reasons. That gave a boost to<br />

the maintenance evolution in this period.<br />

Maintenance norms and standardization<br />

were progressively implemented, and<br />

they became necessary to train and certify<br />

technicians. The first trainings and certifications<br />

were implemented in this period.<br />

Precision maintenance slowly became<br />

common practice, extending the lifecycle<br />

of assets by integrating proper procedures<br />

and standards during installation or repair<br />

of equipment.<br />

From the 80’s till early 2000<br />

During this period maintenance and reliability<br />

engineering methodologies start becoming<br />

common practice in industry. This<br />

is the era of predictive maintenancewhere<br />

maintenance engineers are “prepared for<br />

what will happen next”.<br />

Surging globalisation gave rise to<br />

a further evolution of equipment and<br />

technology. Between 1980 and 2000, the<br />

industrial world changed in many areas:<br />

IT, maintenance, purchasing, communications,<br />

production, quality, safety. In<br />

the maintenance world “optimization”<br />

became crucial to survive. New concepts<br />

such as total productive maintenance<br />

(TPM), total quality management (TQM)<br />

and “lean” originating from Japan were<br />

implemented. The industrial sector was<br />

forced to modernize and adapt to secure<br />

their place on the globalizing market. It<br />

is then that computer maintenance management<br />

systems (CMMS) and quality<br />

management standards such as ISO-9000<br />

(1987) and others were implemented.<br />






Many industries focused on increasing<br />

production and lowering production<br />

costs. So, equipment reliability and availability<br />

became of the utmost importance.<br />

Early detection of failures, increasing<br />

meantime between failures (MBTF), reducing<br />

the meantime to repair (MTTR),<br />

performing pro-active maintenance activities<br />

became the focus of maintenance<br />

departments.<br />

Engineering departments started taking<br />

maintainability and reliability considerations<br />

into their design, extending<br />

the asset’s life cycle. The awareness grew<br />

that the maturity and quality of a maintenance<br />

program is determined by being<br />

ahead of the P-F curve.<br />

During this period industry and their<br />

maintenance departments were confronted<br />

with increasingly and numerous<br />

hurdles. Apart from the fact that economic<br />

drivers put margins under pressure<br />

and cost control was often affecting<br />

maintenance departments first, maintenance<br />

also struggled, and even so today,<br />

to attract young and skilled people. This<br />

is mainly due to a lack of good image and<br />

the true understanding of the job content<br />

and how rewarding it is.<br />

Although much has happened since<br />

the start of the industrial revolution the<br />

past 200 years, it appears that the most<br />

dramatic changes have occurred within<br />

the last 30 years. Especially from the<br />

year 2000 onwards (the IT bug-century<br />

flip) to date, innovations in technology<br />

32 maintworld 2/<strong>2021</strong>

P<br />

The foundational elements of maintenance & reliability management<br />

will never change. But the way technology can deliver value to asset<br />

management programs is changing faster than ever.<br />

The Grain combines its expertise in industrial asset management<br />

and data science to enhance the performance and reliability of your<br />

assets by building customized, accessible and scalable AI solutions.<br />

F<br />

It is our mission to facilitate day-to-day work of maintenance<br />

practitioners, reliability engineers and operators.<br />

We embed the power of advanced analytics to accelerate the process<br />

of learning by combining signal data, maintenance logs or any other<br />

operating context information to predict the asset behavior, add<br />

new insights enabling you to prepare the right actions at the right<br />

time. We believe that blending artificial and human intelligence is<br />

key to exponential performance of your assets.<br />

Welcome to the age of prescriptive maintenance.<br />

Want to be part of it?<br />

Find out more on www.thegrain.pro/innovators or call +32 3 376 33 50<br />

Industrial AI applications


in every area of life are more than they<br />

have ever been in human history and<br />

maintenance management is not left out.<br />

A growing awareness of the value of continuous<br />

improvement and optimised asset<br />

management became obvious. During<br />

this period, an accelerated shift and interest<br />

in developing failure mode driven<br />

equipment maintenance plans, boosted<br />

predictive maintenance.<br />

From early 2000 to 2025<br />

The period where maintenance is evolving<br />

from “analysis to analytics” – the<br />

quantum leap. A new era of prescriptive<br />

maintenance where asset managers, maintenance<br />

managers and operators jointly<br />

use artificial intelligence to predict asset<br />

behaviour and define “what’s the best that<br />

can happen”. We are gradually entering a<br />

period where algorithms predict and allow<br />

automated self-optimisationactions and<br />

humans get a new role defined!<br />

Leveraged by accelerated technology<br />

evolutions and driven by globalised market<br />

mechanics and a fast-changing world<br />

of “instant” and “green” expectations,<br />

the need for continuous perfection and<br />

optimisation have now become prime for<br />

various industries and are key to survive.<br />

More than ever this new truth and the<br />

need to transform from flexible production<br />

to agile production becomes clear,<br />

now that mass production is quickly moving<br />

towards mass customisation. Current<br />

strategies are being questioned and many<br />

amongst us find ourselves at the tipping<br />

point between exploitation and exploration<br />

of our businesses and individual roles,<br />

often leading to new business models.<br />

Even though one already understood<br />

that the true value of asset management<br />

lies beyond the physical asset itself, it now<br />

becomes possible to connect asset data<br />

with context-, process and organizational<br />

data in a better and more efficient manner.<br />

Or to phrase in our language: “Failures<br />

leading to losses due to technical problems,<br />

generally referred to as special cause<br />

losses, can now be blend with operational<br />

inefficiency parameters, referred to as<br />

common cause losses. Here lies the new<br />

window of opportunity to excel, improve<br />

and gain that last overall equipment effectiveness<br />

(OEE) or total plant performance<br />

improvement.<br />

With the emergence of Industrial IoT<br />

(IIoT) collecting data from equipment is<br />

moving from paper-based, excel sheets<br />

and manual inspections to fully automated<br />

systems, enhancing both data quality and<br />

quantity. IIoT enables remote asset monitoring,<br />

also exponentially increases the<br />

quantity and variety of parameters that<br />

can be monitored and this at a better cost.<br />

With artificial intelligence (AI), predictive<br />

analysis (deductive) will shift to prescriptive<br />

analytics (forecasting), allowing us<br />

to move from ‘what happened’ to ‘what’s<br />

the best that can happen’, improving total<br />

plant performance instead of only uptime.<br />

In this early 21st century period, driven<br />

by advanced analytics, we can notice two<br />

major strategic shifts in maintenance<br />

strategies.<br />

A first strategic shiftis one that moves<br />

our maintenance practice from a failure<br />

mode driven asset strategy to an OEE<br />

driven strategy. By combining equipment<br />

data with process data, quality data, performance<br />

data, contextual-operational<br />

data and other relevant information, artificial<br />

intelligence provides new insights<br />

in equipment and process behaviour. This<br />

will provide maintenance technicians,<br />

planners and operators with comprehensive<br />

new, accurate and real-time insights<br />

into asset performance, risks, (..) and allow<br />

them to maintain higher levels of asset<br />

availability.<br />

Root cause analysis (RCA), by applying<br />

fi. fault-tree analysis as well as causeand-effect<br />

or failure-modes-and-effects<br />

analysis (FMECA), is a fundamental part<br />

of any organization’s maintenance and<br />

reliability strategy. Today, however, these<br />

activities are often conducted manually,<br />

and their outcomes are rarely recorded<br />

in a centralized manner. Hence through<br />

digitization and advanced analytics these<br />

methodologies will be automated and updated<br />

continuously. Possible root cause(s)<br />

will be suggested by the system in which<br />

the maintenance expert and/or operator<br />

will acknowledge the ‘real’ root cause, so<br />

improving the accuracy of the analytical<br />

models. Similarly, this can also be applied<br />

for reliability centred maintenance<br />

(RCM), helping teams choose the right<br />

maintenance strategy for each (critical)<br />

equipment. This is a gamechanger in predictive<br />

maintenance.<br />

A second strategic shiftis moving from<br />

an OEE-driven to an Operational Excellence<br />

driven strategy. Combining OEE<br />

data with loading, planning and product<br />

supply chain data allows to improve total<br />

plant performance. Maintenance schedules<br />

will be aligned with production schedules<br />

to optimise total production, balancing<br />

demand and supply. Using AI within<br />

production enables now to shift from mass<br />

production to mass customization.<br />

Further integration of the digital and<br />

physical world enables close interaction<br />

between machines, algorithms and humans.<br />

New digital maintenance execution<br />

systems augmented (AR) and virtual reality<br />

(VR) will support technicians in real-<br />

34 maintworld 2/<strong>2021</strong>


time to perform standardized repairs in a<br />

safe way by using the right tools, procedures<br />

and instructions. Know-how will be captured<br />

digitally, visualised and made available<br />

for everybody. Operators will receive<br />

optimized setpoints for their production<br />

lines, can feed the systems with additional<br />

relevant information that will further improve<br />

the analytical model. Digitisation and<br />

humans go “hand in hand”.<br />

From 2025 and beyond:<br />

The future is a concept –<br />

it doesn’t exist.<br />

We now enter the unknown era of exponential<br />

change, new business models and<br />

renewed anthropocentrism. The self-optimising<br />

prophecy now becomes reality.<br />

In spite of what I stated above and<br />

recognising an accelerated evolution and<br />

change in our asset management landscape,<br />

realise this: “we ain’t seen nothing<br />

yet”. Until present evolution, even though<br />

continuously accelerating, still was very<br />

much a linear evolution. The way we think,<br />

work and act are still very much the same<br />

as they always have been. Admitted, we<br />

evolved and matured, got wiser and our<br />

working models went from a very “centralised”<br />

way of thinking, organising and behaving<br />

to a “de-centralised” format in the<br />

‘90s. But the reality today is that change,<br />

boosted by technology is shifting gear into<br />

an exponential acceleration. A mind trap is<br />

that often we overestimate the change that<br />

will occur in the next two years but underestimate<br />

the change that will occur in the<br />

next ten, affecting our strategic judgement.<br />

Think fi. about quantum computing, a<br />

technology rapidly maturing in the shadow<br />

of today already for some seemingly futuristic<br />

scenarios.<br />

As IIoT will make everything and everybody<br />

interconnected, and everything<br />

is continuously changing and interacting<br />

in these new agile environments, we need<br />

to accept and embrace new distributive<br />

models. Boundaries becoming vaguer, our<br />

world and our industries will continue<br />

to converge. We are undergoing an exponentially<br />

accelerated merge between the<br />

physical and the digital world. Therefor we<br />

need to escape from our traditional way of<br />

thinking, working and organising within<br />

our maintenance or production silo, and<br />

step into this volatile and complex new environment.<br />

We need to re-invent ourselves,<br />

our methods and our solutions and stop<br />

ignoring this change by progressing in a<br />

stubborn linear pattern: “We cannot solve<br />

our future problems with the same way of<br />




thinking we used when creating them.”<br />

Based on real-time information we<br />

will be able to accurately predict and<br />

balance supply and demand improving<br />

our company’s bottom-line without<br />

compromising the quality of service and<br />

reach an almost optimum performance,<br />

by increasing productivity and profit to<br />

unseen limits. The newly required skills<br />

often go beyond our human capacity and<br />

that’s where technology gives us a helping<br />

hand. It is up to us, people, asset managers<br />

to question, master and control that<br />

change.<br />

Blockchain technology will verify<br />

every step of the production process or<br />

provide full transparency in contracts between<br />

companies, suppliers and vendors.<br />

Digital fingerprints (encoded asset certificates)<br />

of each asset and process will be<br />

created, ensuring reliable and qualitative<br />

information. All information will be indisputably<br />

and incorruptibly recorded in<br />

appropriate registers, ensuring increased<br />

security of information. Smart automation<br />

will help us to transform processes<br />

that require interaction, data interpretation<br />

and decision making. Robotics will<br />

allow us to perform smart operations by<br />

automating processes.<br />

I could continue and elaborate on many<br />

other new technologies, but the moral of<br />

this story is: “nothing has changed, but<br />

everything changes”. Allow some slight<br />

exaggeration here, but we are not inventing<br />

anything new, though. We’re just using<br />

old, improved algorithm technologies from<br />

the 9th and 19th century. Just like we used<br />

thousands of years ago hieroglyphs to communicate<br />

between different tribes, today<br />

we call them “emojis”. Wrenches will probably<br />

still be used, but with an improved design<br />

or attached-integrated to a robot. And<br />

algorithms will not be calculated by human<br />

brains but will run on supercomputers.<br />

Conclusion: “Prediction is very difficult,<br />

especially if it’s about the future …”<br />

At “The Grain” we strongly believe that<br />

combining artificial and human intelligence<br />

is key for future success of both our<br />

industry and humanity, hence our wellbeing<br />

and our welfare. The role of people<br />

will remain strategic, only the content of<br />

our jobs will change. Thanks to technology<br />

we can get rid of repetitive and often boring<br />

routine work, in many cases leading to malpractices,<br />

and concentrate on rewarding<br />

and true valuable input. Or to put it simple,<br />

“We are for questioning, AI for answering”.<br />

Einstein already stated it clear: “If I had an<br />

hour to solve a problem, I'd spend 55 minutes<br />

thinking about the problem and five<br />

minutes thinking about solutions.” Hence,<br />

Technology provides the know-how, humans<br />

the know-why.<br />

The most important impact of technology<br />

is how it changes people. Therefore,<br />

it is at its best when it brings people together<br />

and services our purposes! Hence,<br />

we should not be concerned about the exponential<br />

change in artificial intelligence<br />

or robotics, but more about the stagnant<br />

attitude in human intelligence. Beware<br />

however, it is not the robots taking over,<br />

but it is the men who play with toys that<br />

are to be feared. That’s were ethics enters<br />

into the equation. Technology is ethically<br />

neutral, until wrongly applied by us!<br />

Let us embrace technology and use it for<br />

the right purpose with a clear anthropocentric<br />

focus, i.e. for the use and benefit<br />

of humanity and in this case our maintenance<br />

engineers, operators and asset<br />

managers in general. Allow me to quote<br />

Einstein once more, confirming that indeed<br />

nothing really changes: “Why does<br />

this magnificent applied science which<br />

saves work and makes life easier bring us<br />

so little happiness? The simple answer<br />

runs: Because we have not yet learned to<br />

make sensible use of it.”<br />

Well dear Albert, we might get there.<br />

Humans will remain human, technology<br />

will just leverage our skills, if done<br />

right! Man, and machine will keep living<br />

and working side by side like they’ve<br />

done for ages, but now for the better. AI<br />

will provide answers and insights to our<br />

questions in maintenance, process and<br />

organisation. Thanks to AI, we have the<br />

opportunity to embrace that necessary<br />

change. We are heading for a renewed<br />

era of Uber-anthropocentrism, unlocking<br />

human potential and driving a new<br />

industrial renaissance. Human ingenuity<br />

will excel!<br />

[1] As a matter of facts algorithms can<br />

be traced back to the 9th century and<br />

linked to the Persian mathematician-astronomer<br />

Abdullah Muhamad bin Musa<br />

al-Khwarizmi, father of algebra(..)<br />

2/<strong>2021</strong> maintworld 35


Measurement Traceability for<br />

the Controlled Environment<br />

JUSTIN WALSH, Business Development Engineer, Vaisala Inc.<br />

The measurement of environmental<br />

conditions is increasing<br />

in relevance and influence<br />

in industry. As efficiency<br />

standards and capabilities<br />

continue to evolve, these<br />

conditions are being better<br />

monitored and controlled to<br />

optimize performance in a<br />

number of applications. The<br />

amount of information available<br />

and its use for analytics is<br />

growing exponentially, making<br />

it more important than<br />

ever to base decisions on accurate<br />

and reliable data.<br />


for granted, the data from environmental<br />

sensors for parameters such<br />

as relative humidity, temperature and<br />

carbon dioxide provide powerful control<br />

understanding to the operations<br />

they serve. Relevant examples are data<br />

centers, who rely on accurate humidity<br />

and temperature measurement to<br />

protect their IT infrastructure, as well<br />

as the CO2 sensors responsible for the<br />

efficiency and agility of a Demand Controlled<br />

Ventilation (DCV) system in a<br />

school or office. While the general task<br />

of these sensors is not especially difficult,<br />

problems from sensor failure or<br />

inaccuracy can have a large impact on<br />

critical operations. Measurement underperformance<br />

can lead to decreased<br />

efficiency, non-compliance of operating<br />

conditions, unplanned down-time,<br />

or production loss.<br />

Maintaining accurate measurements<br />

is the key to long-term operational efficiency,<br />

and Computerized Maintenance<br />

Management Systems (CMMS) can use<br />

sensor data from more locations and in<br />

greater detail than ever before. Incorporating<br />

environmental sensor data<br />

into these systems and others, such as a<br />

digital twin, or Industry 4.0 and IoT networks,<br />

affect the analytics and predictive<br />

indicators that we gain from those systems.<br />

This places more responsibility on<br />

the measurements from these sensors,<br />

and our efforts to maintain them to a<br />

high standard.<br />

Information from the calibration of<br />

humidity, temperature and CO2 sensors<br />

can be tracked to provide insight into<br />

sensor health and stability that will improve<br />

preventive maintenance schedules<br />

and to help mitigate risk. Digitalization<br />

of these operations relies on data from<br />

36 maintworld 2/<strong>2021</strong>


sensors whose readings will inherently<br />

drift in their accuracy, requiring calibration<br />

and adjustment to maintain peak<br />

performance.<br />

Calibration ensures<br />

data integrity<br />

All measurement sensors are going to<br />

lose accuracy over time, through normal<br />

continuous use or environmental<br />

influences. One of the best ways to understand<br />

an instrument’s measurement<br />

performance is to assess its accuracy, and<br />

calibration is the only way to definitively<br />

determine how accurate these instruments<br />

are. During calibration, it is determined<br />

how much, if at all, the measurement<br />

deviates from a defined reference,<br />

and adjustments can be made to preserve<br />

measurement accuracy, and quality data.<br />

The maintenance activities for some<br />

of these sensors can pose challenges of<br />

their own, as it can be inconvenient or<br />

impossible to remove and send an instrument<br />

to a calibration lab for service.<br />

In many cases it is preferred to calibrate<br />

in-situ with a spot-check or in-house<br />

calibration laboratory. An example of<br />

this is in cleanrooms, where the transport<br />

of equipment in and out of the<br />

space is a time-consuming process. Difficulty<br />

lies in performing that calibration<br />

or adjustment to a high standard,<br />

as not all calibration tools are the same,<br />

and the quality of references is of decisive<br />

importance.<br />

The right tool for the job<br />

Portable reference equipment can<br />

provide a versatile and cost-effective<br />

way to maintain sensors at a high<br />

level by using them as transfer standards.<br />

They can be of high accuracy<br />

that meet or exceed the unit being<br />

evaluated and maintain traceability<br />

to international standards. Transfer<br />

standards are used to transfer a<br />

measurement parameter from one organization<br />

to another, from a primary<br />

standard to a secondary standard, or<br />

from a secondary standard to a working<br />

standard in order to create or<br />

maintain measurement traceability.<br />

Traceability is the relating of<br />

the measurement back to the international<br />

system of units (SI units)<br />

through an uninterrupted chain of<br />

comparisons, all with stated uncertainties.<br />

Establishing a high degree of<br />

confidence in the measurement becomes<br />

even more important for portable<br />

instruments, as their accuracies<br />






do not often exceed that of the units<br />

under test. It is very difficult and expensive<br />

to maintain reference equipment<br />

of higher accuracy than many<br />

high end humidity, temperature, barometric<br />

pressure, and CO2 sensors<br />

for occupied condition monitoring.<br />

They are likely to be a 1:1 comparison,<br />

or slightly better, but rarely meeting<br />

the traditional 4:1 test accuracy ratio.<br />

Therefore, it is of great importance<br />

that your instrument be calibrated<br />

against an SI-traceable reference to<br />

ensure the quality of measurement<br />

data. Additionally, proper procedure<br />

and best practices must be followed,<br />

because even the best reference used<br />

improperly can degrade accuracy.<br />

Further increasing the trustworthiness<br />

of a traceable calibration<br />

is the accreditation of the service<br />

provider that has calibrated the reference<br />

standard or transfer standard.<br />

ISO/IEC 17025 accredited calibration<br />

service providers have been certified<br />

to provide traceable calibrations with<br />

detailed uncertainty information,<br />

proper environmental conditions,<br />

and methods by competent personnel.<br />

In order to ensure these high<br />

standards, these laboratories are audited<br />

regularly.<br />

Assessing traceability<br />

How do you know if your instrument is<br />

indeed SI-traceable? One way is to study<br />

its calibration certificate. For example,<br />

the following information should be<br />

available:<br />

1 Calibration results include measurement<br />

uncertainties<br />

2 All calibration references are identified<br />

3 Notes on how uncertainties are<br />

determined and what uncertainty<br />

sources are included<br />

4 Description of how the SI traceability<br />

was established<br />

5 Reference and ambient conditions<br />

Conclusions<br />

The more important and integrated<br />

the sensor data is to your operation,<br />

the greater the assurance is needed<br />

for that data to be accurate and verifiable.<br />

It is encouraged that equipment<br />

and facility managers select the best<br />

methods and providers when planning<br />

their calibration and maintenance<br />

activities.<br />

2/<strong>2021</strong> maintworld 37


05-Steps to Develop Drilling<br />

Organization Asset Integrity<br />

Management Program<br />

Asset Integrity is the ability of an asset to perform its function effectively<br />

and efficiently throughout its lifecycle while safeguarding life and the<br />

environment. In other words, an asset in question is required to perform its<br />

intended function as per the design intent. An Asset Integrity management<br />

system is about taking care of the aspects of people, processes and<br />

equipment interaction effectively to achieve the design intent of an asset.<br />


Asset Reliability & Integrity Management Division, Consulting Service Department, Saudi Aramco, Dhahran, Saudi Arabia<br />

38 maintworld 2/<strong>2021</strong>





failure of one or more factors related to<br />

people, process and equipment. To achieve<br />

an effective asset integrity, the organization<br />

needs to have more focus on major accident<br />

hazards as these incidents are rare, but the<br />

consequence is large. Drilling operations<br />

are exposed to a number of hazards such as<br />

corrosion, fatigue, accidental damage, extreme<br />

weather conditions, geological, geotechnical,<br />

change in used technology etc.<br />

throughout their life cycle which may lead<br />

to a major accident. Drilling operation may<br />

also have additional complexities due to the<br />

involvement of many stakeholders, such<br />

as drilling contractor, drilling equipment<br />

supplier, operator etc. It therefore becomes<br />

more important for drilling to develop a<br />

structured asset integrity management<br />

program where the proper integration of<br />

different stakeholders is established with<br />

clear roles defined to reduce the probability<br />

of hazard realization from a major accident<br />

hazard (MAH). This brings the organization<br />

focus on managing the people, process<br />

and equipment/machinery-related weakness<br />

to avoid the MAH and ensures that the<br />

asset functions as per the design intent.<br />

In this article, A 05-step process is suggested<br />

to develop an overall Asset Integrity<br />

Management program for a drilling organization<br />

to manage a Major Accident Hazard<br />

threat and its realization.<br />

The operation of a drilling company is<br />

more complex as several stakeholders, such<br />

as operator (drilling, reservoir, geological,<br />

operations team), Drilling service companies<br />

(drilling fluids, cement, BOP manufacturer,<br />

well casing design, drilling bits<br />

etc.), classification societies and Drilling<br />

company/contractor (Rig design & maintenance,<br />

driller, rig crew etc.) are involved<br />

in achieving the common goal of drilling.<br />

Due to the very nature of a drilling operation,<br />

the organizations need more focus on<br />

major accident hazards as these incidents<br />

are rare, but the consequence is large. A<br />

major accident hazard is the failure of one<br />

or more factors related to people, process,<br />

and equipment. While drilling organisations<br />

manage control and critical drilling<br />

equipment well, based on proponent<br />

in-house standard and/or international<br />

standard such as API/ISO, the complexity<br />

and various stakeholder involvement often<br />

lacks the availability of a structured asset<br />

integrity program to manage the major accident<br />

hazard. An asset Integrity Management<br />

Program in any organization requires<br />

the integration and utilization of company/<br />

stakeholder’s internal process, procedure,<br />

standard & guidelines and tools e.g. forms,<br />

checklist etc. for proactive control & mitigation<br />

of a major accident hazard.<br />

In this paper, A simplified 05-step process<br />

is proposed for a drilling organization<br />

to develop an organization-specific Asset<br />

Integrity Management program.<br />

Step 1- Development of<br />

Asset Integrity Management<br />

Framework<br />

An organization should develop a unified<br />

corporate Asset Integrity Management<br />

framework based on asset management, inspection,<br />

maintenance & operation requirements.<br />

The framework should detail the<br />

integration of a drilling company/contractor<br />

internal process/procedure and other<br />

stakeholder process/procedure to achieve<br />

avoidance/mitigation of a major accident<br />

hazard.<br />

The framework should define the people,<br />

process and equipment/system integrity<br />

requirements to avoid a potential major accident<br />

hazard by evaluating following basic<br />

aspects e.g.<br />

• Do we understand what can go wrong?<br />

• Do we know what systems are in place<br />

to prevent this from happening?<br />

• Do we have assurance and verification<br />

functions that these systems will work?<br />

• Do we have proactive visualization of<br />

integrity performance based on leading/lagging<br />

KPI?<br />

Based on the evaluation of the above<br />

aspects, the framework should include<br />

a high level requirement of the following<br />

Integrity management element<br />

2/<strong>2021</strong> maintworld 39


• Policy for Asset Integrity Management<br />

and utilization of any current/<br />

existing management system to ensure<br />

Asset Integrity<br />

• Structure & governance of asset<br />

integrity management to effectively<br />

manage the asset integrity related to<br />

activity and function<br />

• Identification of barrier and its<br />

component e.g., key competency,<br />

safety critical element (equipment/<br />

component/system), critical process<br />

/ procedure, interface requirements<br />

for drilling service and function.<br />

• Development of Assurance-function<br />

based on existing operation, engineering,<br />

technical, maintenance<br />

requirements<br />

• Verification requirements/activity<br />

based on regular review of current<br />

integrity status (Use existing forms/<br />

checklist, program to the maximum<br />

extent)<br />

• Visualization of existing rig integrity<br />

status based on identified Safety<br />

critical element (SCE) integrity status<br />

and current status review<br />

• Performance monitoring based on<br />

leading and lagging KPI related with<br />

asset integrity management.<br />

Step 2- Develop Asset<br />

Integrity Management<br />

Structure<br />

Drilling company/contractor should ensure<br />

that the existing management program<br />

provide the integrity management<br />

structure and function to specifically<br />

support the verification of safety critical<br />

equipment/element (SCE) assurance<br />

functions to avoid/mitigate MAH. This<br />

should include an asset Integrity Management<br />

team both at corporate/site<br />

level with details RASCI (responsible,<br />

accountable, support, consultative and<br />

information) chart to execute the Integrity<br />

management specific task leading to<br />

adequate focus on major accident hazard<br />

and related risk realization.<br />

The company should create an Identify<br />

Asset Integrity Management/Engineering<br />

team, and assign responsibilities<br />

to address specific asset integrity-related<br />

activity/task such as:<br />

• Development, execution and monitoring<br />

of asset integrity management<br />

specific internal procedure & guide<br />

• Operational risk management guide,<br />

risk register development and utilization<br />

of risk register as a tool to<br />

manage the risk.<br />

• Development & utilization of Well<br />

barrier schematic for different types<br />

of Well, and an area to support asset<br />

integrity<br />

• Evaluation of MHA related safety<br />

critical task analysis (SCTA) and its<br />

effectiveness through assurance &<br />

verification process to minimize human<br />

error.<br />

• SCE (people, process, equipment/<br />

system) identification and its management<br />

based on SCE performance<br />

standard development, execution<br />

& update. Below is a list of some required<br />

performance standard examples<br />

• Well test and control<br />

• Blow out preventer system<br />

• Choke & Kill system<br />

• Rig move & structure<br />

• Main power system & supply<br />

• Emergency power system & supply<br />

• Fire and gas detection<br />

• Third party equipment<br />

• Emergency shutdown system<br />

• Lifting Equipment<br />

• Execute, verify & monitor SCE integrity<br />

assurance based on existing<br />

Inspection, Maintenance, testing<br />

(IMT) and operating procedures<br />

• SCE (both hardware and software)<br />

Performance monitoring and continuous<br />

improvement<br />

Step 3- Create effective risk<br />

management process to avoid<br />

Major Accident hazard<br />

The organization should develop a decision<br />

support flow diagram with inbuilt<br />

action details for all the key causal risk of<br />

any major hazardous event (Refer Figure-1)<br />

to develop an effective risk management<br />

for asset integrity. This flow diagram<br />

will provide the necessary insight<br />

about the inclusion of required assurance<br />

and verification task related with process,<br />

people and equipment in the applicable<br />

performance standard of identified SCE.<br />

To have a detailed insight about the<br />

availability of control, and mitigation<br />

barrier of an identified causal risk, an<br />

organization can use other safety study<br />

tools such as SWIFT (Structured what<br />

if technique), Bow-tie analysis etc. The<br />

generated information can be used to<br />

develop a risk register at rig level with<br />

existing control and mitigation details<br />

on all the operation, process, mechanical,<br />

and people-related causal risks. The<br />

developed risk register can be aligned<br />

with the overall ERM (Enterprise Risk<br />

Management) process of the organization<br />

and used as a tool for SCE integrity<br />

management with focus on control &<br />

mitigation effectiveness. A risk tracking<br />

system should be available to monitor<br />

the status of identified risk based on the<br />

listed control & mitigation barrier/SCE<br />

effectiveness. The risk management<br />

process should define a clear RASCI<br />

(Responsible, Accountable, Support,<br />

Consultative and Information) chart for<br />

the employee/management and devise<br />

a mechanism to review and update the<br />

identified risk on a regular basis.<br />

Step 4- Establish Asset<br />

Integrity Model Barrier and<br />

SCE management process<br />

Drilling company/contractors should<br />

develop a barrier model for people, process<br />

and Well control/critical equipment<br />

Figure-1 Decision support flow diagram based on hazard<br />

40 maintworld 2/<strong>2021</strong>

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(both well and rig related) in some form of<br />

barrier model to have visibility and clarity<br />

of any hazard/threat realization concept<br />

across the organization. It is worth mentioning<br />

that the representation of hardware<br />

equipment/SCE in the form of a rig<br />

barrier along with Well barrier schematic<br />

(for hardware & software) will provide the<br />

required visualization of a hazard/threat<br />

as an integrity model being followed by the<br />

subject organization operation.<br />

Shown below (Figure-2) is a typical<br />

barrier concept based on the swiss cheese<br />

model which represents the hardware<br />

(equipment) flaws for hazard and threat realization<br />

being followed for Asset Integrity<br />

Management as an AIMS Model representation<br />

for a process plant. Drilling organizations<br />

can develop a similar AIMS model as<br />

applicable.<br />

Further, organizations should develop<br />

a clear process for SCE identification and<br />

its review/update process for the effective<br />

monitoring of a Major accident hazard. A<br />

Safety Critical Element (SCE) is defined<br />

as a system, Item of equipment, Person or<br />

Procedure that is specifically identified<br />

using HEMP (Hazard effect management<br />

process) and included in the facility Safety<br />

Case (highly recommended to develop for<br />

drilling organization) as an asset that:<br />

• Failure of which could cause or<br />

contribute substantially to a Major<br />

Accident or Major Environmental Accident;<br />

and/or<br />

• Purpose of which is to prevent or limit<br />

the effect of a Major Accident or Major<br />

Environmental Accident.<br />

Once an SCE has been identified, the organization<br />

should align the identified SCE<br />

(hardware & software) with any operational<br />

risk as captured in the risk register and<br />

develop a barrier & related safety critical<br />

equipment/system performance standard<br />

based on key IMT (Inspection, Maintenance<br />

& Testing) and operational assurance<br />

activity, to avoid the risk realization.<br />

A performance standard defines the<br />

critical function, specific, verifiable, realistic<br />

and achievable performance requirements<br />

with which the SCE shall comply<br />

throughout the lifecycle of the installation.<br />

It can be expressed in qualitative or quantitative<br />

terms, but should produce a Pass<br />

or Fail result, which is used as the basis for<br />

managing the hazard.<br />

A barrier/SCE management process<br />

should utilize a performance standard,<br />

its verification and reporting along with<br />

other activity based on a PDCA (Deming<br />

cycle) concept. This process should<br />

include all the required instruction and<br />

linking of existing process/procedure,<br />

along with the roles & responsibilities of<br />

the Asset Integrity assurance team, the<br />

OME team and the Asset integrity verifier.<br />

All the identified barrier/SCE should<br />

be verified against these performance<br />

standards to ensure its integrity.<br />

A typical SCE management flow diagram<br />

(Figure-3) based on performance<br />

standard is depicted below for reference.<br />

Step-5 Identify leading and<br />

lagging asset integrity specific<br />

KPIs and develop Asset<br />

Integrity dashboard<br />

The identification of leading and lagging<br />

indicators are key to managing the organization<br />

asset integrity performance factors<br />

proactively that are responsible for major<br />

accident hazard realization e.g. the lack of a<br />

risk management process, deficiency in internal<br />

procedure & guidelines, lack of barrier/safety<br />

critical element management<br />

and lack of compliance audit & verification.<br />

On many occasions, clear boundaries in<br />

a leading and lagging indicator are fuzzy. A<br />

gas kick for example, is a leading indicator<br />

for a possible blow out, whereas it is a lagging<br />

indicator for well bore fluid that has<br />

already entered the well bore due to a barrier<br />

failure.<br />

A leading indicator is considered to<br />

be a predictive set of parameters/course<br />

of action which delivers early information<br />

on barrier performance. It must be<br />

measurable and recognizable and provide<br />

benchmark operation & organization performance<br />

for asset integrity management.<br />

Lagging to leading events for drilling<br />

operation can be presented as progression<br />

arrow define by Nafiz Tamim et. al. (2016)<br />

in Figure-3<br />

Organizations should develop a process<br />

for the identification, review and<br />

monitoring of leading & lagging indica-<br />

42 maintworld 2/<strong>2021</strong>


Figure-3 Transition of lagging to leading indicators in drilling operation<br />

Figure-4 Leading Indicator identification tree with example<br />

tors based on the concept of progression<br />

arrow defined in Figure-3 and below is an<br />

example of a leading indicators identification<br />

tree for drilling operation, Refer<br />

Figure-4 proposed by Nafiz Tamim et. al.<br />

(2016).<br />

Organizations can further evaluate<br />

and adopt the below KPI as applicable for<br />

Asset Integrity performance monitoring.<br />

• Corporate level lagging KPI “Asset<br />

Integrity Index” based on rig level<br />

integrity related KPIs e.g., corrosion<br />

management, process & procedure<br />

Compliance, gas kick, BOP, well control<br />

etc.<br />

• Leading KPIs related with SCE management<br />

and its integrity status<br />

• Number of processes & procedures<br />

in compliance<br />

• Number of anomalies related with<br />

barrier/SCE that were not closed out<br />

by the planned due date at the end of<br />

each quarter<br />

• Number of barrier/SCE not meeting<br />

performance standard<br />

• Number of activations of barrier/<br />

SCE e.g., gas kick<br />

• Number of barrier/SCE past a<br />

planned completion date (barrier/<br />

SCE backlog)<br />

• Number of barrier/SCE did not<br />

follow MOC process<br />

Based on KPI monitoring, an organization<br />

should develop a dashboard for the<br />

clear visualization and reporting of all the<br />

identified SCEs, and an overall integrity<br />

status.<br />

A typical dashboard reporting for<br />

safety critical element based on the swiss<br />

cheese model is represented below in<br />

Figure-5.<br />

Conclusion<br />

Establishing a structured asset integrity<br />

management program is key to managing<br />

a major accident hazard. Using the<br />

05-step process as detailed in this paper,<br />

an organization can develop a drillingspecific<br />

asset integrity program to<br />

achieve excellent performance in overall<br />

asset management, while addressing the<br />

avoidance and mitigation of a major accident<br />

hazard.<br />


[1] Saudi Aramco (2015), Asset Integrity Management System, issued March 2015, Dhahran, Saudi Arabia.<br />

[2] CCPS, 2010. Guidelines for Process Safety Metric, Center for Chemical Process Safety, John Wiley & Sons, Inc.<br />

[3] IOGP, 2011, Process Safety-Recommended Practice on Key Performance Indicators, Report No. 456, International Oil & Gas Producers, UK.<br />

[4] UK Energy Institute (2007), Guidelines for the Management of Safety Critical Elements, Second Edition, Published March 2007, ISBN 978 0 85293 462 3,<br />

[5] UK Health and Safety Executive (2006), Assessment Principles for Offshore Safety Cases (APOSC), issued March 2006,<br />

available online from: http://www.hse.gov.uk/offshore/aposc190306.pdf,<br />

[6] ANSI/API, April 2010. API Recommended Practice (RP) 754. Process safety performance Indicators for the Refining and Petrochemical Industries. API Publication.<br />

2/<strong>2021</strong> maintworld 43


Infrastructures Physical Assets High<br />

Performance Achievement based on<br />

Reliability and Maintenance Program,<br />

A.I and Asset Integrity Management<br />

Nowadays the world invests around $2.5 trillion a year in infrastructure physical<br />

assets such as transportation, power, water, and telecom systems on which businesses<br />

and populations depend. Yet this amount continues to fall short of the world’s everexpanding<br />

needs, which results in lower economic growth (MGI’s 2013 report).<br />


led to enormous spending cuts across<br />

the globe. In Europe, the post-war infrastructure,<br />

especially bridges, is ageing. Despite<br />

that, the maintenance backlog, i.e., the<br />

amount of maintenance and rehabilitation<br />

that should have been completed in order to<br />

maintain infrastructures in good condition<br />

but has been deferred, is growing considerably.<br />

This problem could be being amplified<br />

because of the COVID-19 Pandemic, that<br />

causes further service cancellations, delays<br />

and consequently spending cuts.<br />

This article aims to demonstrate the<br />

importance to implement the reliability and<br />

maintenance program during infrastructure<br />

concept and design phase, as well as A.I<br />

integrated to Asset Management and Asset<br />


CALIXTO,<br />

Eduardo Calixto Consulting<br />

(ECC), RAMS Engineer and<br />

Asset Management Expert,<br />

EFNMS member<br />

Integrity Management during operation<br />

phase.<br />

2 – Reliability and<br />

Maintenance Program for<br />

Infrastructure<br />

The maintenance activities applied to infrastructures<br />

physical assets need to be under<br />

the context of a Maintenance Program, that<br />

considers different reliability engineering<br />

methods to be implemented throughout<br />

the different life cycle phases from concept<br />

to the decommissioning phases.<br />

The Reliability Centred Maintenance<br />

(RCM) is a method initially applied<br />

during the design phase, that aims<br />

to define the maintenance tasks based<br />

on the infrastructure failure modes,<br />

causes and effects as well as the associated<br />

risk. The information that is input<br />

in order to perform the RCM are the<br />

infrastructure’s failure mode and effect<br />

analysis (FMEA). In the Railway Industry,<br />

the infrastructure’s systems play an<br />

important role in terms of the safety and<br />

performance of the railway. One good<br />

example is the rail component that in<br />

44 maintworld 2/<strong>2021</strong>


case of failure, will trigger impact on a<br />

Railway System’s operational availability<br />

and may trigger a major accident such as<br />

derailment as shown in figure 1.<br />

Figure 1 describes the rail RCM where<br />

the risks are assessed as intolerable based<br />

on the combination of the cause frequencies<br />

and the consequence severity. Based<br />

on such assessment, different maintenance<br />

task types and the frequencies at<br />

which they are carried out are defined to<br />

mitigate the risk, such as Visual inspection<br />

and Track Road Vehicle Inspection<br />

(Ultrasonic Test). Similar methods comparable<br />

to the RCM approach is the RBI.<br />

The Risk Based Inspection (RBI)<br />

is applied initially in the design phase<br />

and later during the operation phase.<br />

The RBI Infrastructure scope focuses<br />

on a failure that can trigger a major<br />

accident. The RBI method is implemented<br />

based on specific procedures<br />

and standards such as: API 580, API 581<br />

and EN 1691, which can be qualitative<br />

or semi-quantitative based on the RBI<br />

application levels.<br />

In addition to qualitative methods,<br />

it is important to have quantitative<br />

analysis to predict the Infrastructure<br />

Physical Assets RAM performance such<br />

as Lifetime Data Analysis, also popular<br />

known as Weibull Analysis, RAM<br />

Analysis and Reliability Growth<br />

Analysis. These methods can be applied<br />

to assess, verify and validate the infrastructure<br />

system’s RAM performance.<br />

However, the Probabilistic Degradation<br />

Analysis (PDA) is more<br />

appropriate to predict such an infrastructure’s<br />

reliability performance. The<br />

PDA aims to define the infrastructure’s<br />

physical asset reliability based on integrity<br />

degradation failure data related<br />

to the thickness of a crack, corrosion<br />

and erosion measured by non-destructive<br />

test methods. By applying these<br />

methods, it is possible to predict when<br />

the functional failure will be achieved<br />

based on the trend of degradation such<br />

as thickness or depth (Crack or Corrosion)<br />

and by considering the degradation<br />

limit as shown in figure 2.<br />

The pink line 1.5 mm in figure 2 is the<br />

limit of corrosion, where a functional<br />

failure is expected to occur. The other<br />

different lines are different measurements<br />

that predict the trend of the<br />

evolution of corrosion a different points<br />

in time. Therefore, if we project the<br />

interception of each of these lines with<br />

the straight line (1.5 in Y axis) in x axis,<br />

there will be different times of functional<br />

failures. These functional failure times<br />

are used to predict the reliability and<br />

the failure rate function. Based on such<br />

information it can be defined when the<br />

inspection needs to take place.<br />

Figure 1: Rail FMEA/RCM ECC Database. Source Eduardo Calixto <strong>2021</strong>.<br />

Figure 2: Probabilistic Degradation Prediction. Source: Calixto E, <strong>2021</strong> – Software<br />

Weibull++ HBK.<br />

3 - Maintenance 4.0 applied<br />

for infrastructures.<br />

Artificial Intelligence (A.I) aims to enable<br />

a machine to think and make its<br />

own decisions based on data collected<br />

and assessed automatically without<br />

any human intervention. Based on the<br />

EFNMS – European Committee Maintenance<br />

4.0 (ECM4.0) <strong>2021</strong>, Industry 4.0<br />

is a new paradigm and the last industrial<br />

revolution, that has been implemented<br />

across the globe intensively in the past<br />

five years and is supported by the utilization<br />

of Enabling Digital Technologies<br />

named 4.0.<br />

Concerning the application of A.I for<br />

Infrastructures Physical Assets, machine<br />

learning is applied for the equipment<br />

criticality and critical alert levels classification,<br />

failure regression predictions and<br />

the automatic application of the Prognostic<br />

Health Management (PHM). In<br />

the case of an Infrastructure system, the<br />

stress factors measured by sensors are<br />

vibration, voltage, temperature, humidity;<br />

Non-destructive test measurements<br />

are also taken such as crack thickness,<br />

corrosion depth and other physical parameter<br />

that lead equipment degrade to<br />

functional failure.<br />

The Deep Learning (DL) methods, a<br />

special type of Machine Learning, can<br />

also be applied to support the preventive<br />

maintenance of an Infrastructure<br />

System. The DL is a more sophisticated<br />

machine learning method, that applies a<br />

deep neural network that encompasses<br />

2/<strong>2021</strong> maintworld 45


several hidden layers as shown in figure<br />

3. The principles of Deep Neural network<br />

consider different layers such as Convolution<br />

Layer, Pooling Layer, ReLu, Fully<br />

Connected, Softmax and the output image<br />

classification. (https://www.eduardocalixto.com/paper-<strong>2021</strong>/)<br />

Despite the advantage of applying A.I<br />

Deep Machine Learning and other A.I<br />

methods as well as reliability engineering<br />

methods, it is necessary to integrate such<br />

methods in an Enterprise Asset Management<br />

System. This will enable the management<br />

of the preventive maintenance<br />

tasks defined for all these methods and<br />

will ensure that the proper resources are<br />

allocated in the proper time to mitigate<br />

the Infrastructure Physical Asset risk of<br />

unavailability along time and the possibility<br />

of a major accident. The next item will<br />

discuss the Asset Integrity Management<br />

as part of the Asset Management.<br />

4 - Infrastructure Asset<br />

Integrity Management (AIM)<br />

An Infrastructure Physical Asset integrity<br />

failure may lead to unavailability, or a major<br />

accident with multiple fatalities. Therefore,<br />

the so-called safety critical elements (SCE)<br />

are the physical assets, which in case of failure,<br />

may lead to a major accident such as jet<br />

fire, toxic cloud release, explosion, fire, toxic<br />

product spill, aircraft crash, trains collision<br />

or derailment. In fact, a major accident can<br />

be triggered by Infrastructure integrity failure,<br />

software, hardware or human error or a<br />

combination of such factors.<br />

In order to mitigate such risks of a major<br />

accident it is necessary to implement a<br />

Reliability & Maintenance (R&M) Program<br />

immediately at the first stage of the concept<br />

and design of a physical asset’s life cycle and<br />

implement all recommendations from such<br />

R&M methods. After that, it is necessary to<br />

implement the risk management and inspection<br />

& test program concerning the A.I<br />

technologies during the operation phase.<br />

The Asset Integrity Program can apply<br />

the same elements of the AM defined in ISO<br />

55000 such as context of the organization,<br />

leadership, planning, support, operation,<br />

and performance evaluation but needs to<br />

focus on the critical safety elements management.<br />

Since 2010, the new era of Industry 4.0<br />

has become a reality for many industries<br />

across the globe. In the last five years new<br />

IOT technology development has been<br />

integrated with EAM solutions concerning<br />

technologies such as Big Data, PHA and Machine<br />

Learning, Reliability 4.0 and the usual<br />

Figure 3: Flow Chart of A.I Deep Learning applied for Bridge.<br />

Source: Mohammad Noori.0 <strong>2021</strong>.<br />

Figure 4: AIM flow into AM Process. Source: Stewart Paul. Integrity PRO, Enkelt,2018<br />

maintenance management routine.<br />

Despite all development that enables<br />

integrated AM, too much focus has been<br />

given to availability performance and<br />

maintenance, with a lack of effort for safety<br />

concerning the safety critical element<br />

management.<br />

However, it is very important to establish<br />

a process to enable an effective AIM<br />

flow integrated to the AM process. Figure<br />

4 describes the AM and AIM flow, highlighted<br />

in green, as part of the AM flow. In<br />

the case of AIM, safety management takes<br />

place in the fourth step, which encompasses<br />

the safety routine management<br />

(safety meeting and incident reports)<br />

as well as the Barrier Management. The<br />

Safety Barrier model is part of the barrier<br />

management that defines the level of risk<br />

of each SCE automatically, based on real<br />

online data.<br />

Since the SCE is defined based on previous<br />

risk analysis considering severity<br />

criticality, the Risk Management of such<br />

SCE performed by the Barrier Model is<br />

automatically updated, enabling Asset Integrity<br />

and helping Safety managers manage<br />

the risk of the SCE on a daily basis.<br />

The Infastructure’s Physical Assets<br />

need to have in the end, all information<br />

integrated in an EAM that encompasses<br />

the best A.I technologies and reliability<br />

engineering methods to enable the leaders<br />

to make a fast and reliable decision.<br />

46 maintworld 2/<strong>2021</strong>




A Virtual Case Studies Event<br />

June 23-24, <strong>2021</strong>



Principal Consultant,<br />

Logio S.R.O.<br />

Part 3<br />

Monetizing Data<br />

in Maintenance:<br />

Data-driven Spare Parts Management<br />

Management of spare parts and other materials<br />

needed for realization of maintenance processes<br />

is one of the key functions in physical asset<br />

management. Especially in power generation, oil<br />

and gas and heavy chemical industries, spare<br />

parts inventories can easily add up to tens<br />

of thousands of various items, in a value of<br />

hundreds of millions of euros.<br />

EFFICIENT SPARE PARTS inventory management<br />

can have significant impact on the<br />

financial performance of the company. Better<br />

spare parts management can lead to improvement<br />

of financial performance of the<br />

company. Spare parts inventory can lock<br />

in significant amounts of working capital.<br />

This article summarizes recommendations<br />

for effective spare parts inventory management<br />

and spare parts optimization using<br />

various sets of data and statistical analytical<br />

methods.<br />

48 maintworld 2/<strong>2021</strong>


1 Eight rules of good spare<br />

parts management<br />

In our previous research, we refined the<br />

following eight rules – best practices – for<br />

good spare parts management:<br />

1) Focus on preventive maintenance<br />

– for preventive maintenance no<br />

inventories of spare parts need to<br />

be held.<br />

2) Solve problems in spare parts processes.<br />

3) Segment your spare parts portfolio.<br />

4) Evaluate spare parts criticality.<br />

5) Apply suitable forecasting methods<br />

and verify their accuracy and reliability.<br />

6) Use special methods for intermittent<br />

demand items.<br />

7) Treat your master data well: Identification<br />

and naming of spare parts<br />

8) Consider the whole lifecycle of your<br />

assets while making decisions related<br />

to spare parts.<br />

In this issue of <strong>Maintworld</strong> we will focus<br />

on forecasting – the essential element in<br />

inventory management. Follow the rules<br />

5 and 6 to apply suitable forecasting<br />

methods and use special forecasting<br />

methods for spare parts with intermittent<br />

consumption. Add rule 7 to improve<br />

identification and naming of your<br />

spares in master data.<br />

Spare parts management starts<br />

with good forecasting<br />

The next step in the specification of<br />

optimum spare parts inventory management<br />

regime is the prediction of future<br />

consumption of the items in stock. The<br />

forecast is always based on transactional<br />

data from information systems – history<br />

of spare parts consumptions, which must<br />

be representative (meaning sufficiently<br />

long). In the case of spare parts, we usually<br />

work with a history of three to ten years<br />

(depending on industry). Three years of<br />

recorded history seems to be the minimum<br />

for intermittent items. A general<br />

rule here applies: the longer the history,<br />

the better and more reliable the forecast.<br />

When analyzing historical consumption,<br />

we need to carefully distinguish between<br />

material consumed for planned maintenance<br />

(planned shutdowns, turnarounds,<br />

preventive maintenance) and spare parts<br />

issued for unplanned (corrective) maintenance<br />

– repairs. In forecasting, we must adjust<br />

the history for planned maintenance.<br />

In the forecasting process, items should<br />

be treated individually, according to the<br />

character of their consumption. Items with<br />

common demand patterns (high runners<br />

– fast moving items like fasteners, etc.) can<br />

be forecast using a number of standard statistical<br />

methods normally used in inventory<br />

management (moving average, exponential<br />

smoothing, Holt’s exponential smoothing,<br />

trends, seasonal indexes, Winter’s method,<br />

etc.). Items with intermittent demand require<br />

a special suitable method to be applied.<br />

The use of standard methods of prediction<br />

and inventory management in case of intermittent<br />

items results often in a substantial<br />

overestimate of future consumption and<br />

therefore excessive inventory level.<br />

Figure 7: Intermittent demand in maintenance (item: Spiral sealing DN25-40 RF)<br />

Intermittent demand is the pitfall<br />

of spare parts management<br />

One of the specific problems in spare parts<br />

inventory management is the nature of<br />

spare parts consumption - intermittent<br />

demand. If we analyze the consumption<br />

history of a typical spare part, we find that<br />

the historical consumption in most of the<br />

analyzed periods amounted to zero. Such<br />

infrequent or intermittent demand, usually<br />

with demanded quantity of just a few pieces,<br />

is very typical for spare parts and other<br />

maintenance inventories. An example of<br />

the consumption history of an intermittent<br />

demand item is presented in Fig. 9.<br />

In maintenance, intermittent demand<br />

is quite often combined with long supplier<br />




2/<strong>2021</strong> maintworld 49


Figure 9: Validation table with potentially duplicate items found in master data.<br />

leadtimes. For maintenance inventory<br />

management, intermittent demand and<br />

long leadtimes are a tricky complication,<br />

often leading to large overstock. The main<br />

problem with managing and forecasting<br />

intermittent items is that the standard<br />

forecasting methods used for fast moving<br />

goods (for instance moving averages, exponential<br />

smoothing, Holt’s and Winter’s<br />

method, constant or regression models<br />

with seasonal indexes, etc.) simply do not<br />

seem to work for these items. In case of<br />

intermittent consumption, special statistical<br />

methods (such as bootstrapping<br />

or Smart-Willemain method) need to be<br />

applied.<br />

Smart and Willemain (2004) suggested<br />

a stochastic simulation forecasting<br />

method. Using this method it is possible<br />

to specify minimum inventory level (reorder<br />

level) in order to ensure fulfilment<br />

of requirements with target probability<br />

(logistic service level, target of availability).<br />

The method is based on random sampling<br />

from the history of consumption.<br />

In statistics, similar methods are called<br />

bootstrapping.<br />

Besides intermittent items, in a large<br />

maintenance inventory we can also find<br />

fast-moving items with stable and high<br />

regular consumption. These are especially<br />

items of common consumables like fasteners,<br />

generic gaskets, or bearings. For<br />

these items, standard methods of inventory<br />

management and future demand<br />

forecasting can be applied.<br />

Identification of spare parts and<br />

cleaning master data<br />

In spare parts optimization projects, we<br />

quite often face various problems with<br />

quality of spare parts master data – especially<br />

naming: issues with unstandardized<br />

naming or incorrect names, names in various<br />

languages, different word order, typos<br />

etc. hinder significantly all efforts in spare<br />

parts optimization and generally result<br />

in duplicate (or multiplicate) master data<br />

records for identical materials (identical<br />

spare part is stored in several master data<br />

records with different names). In order<br />

to clean spare parts master data, we apply<br />

advanced data analytics on master<br />

data to identify or cluster duplicate (or<br />

similar) items. This key analytical method<br />

is “matching” – comparing names and<br />

certain master data attributes to assess<br />

similarity of spare parts. For this we use<br />

multicriterial comparisons. To analyze<br />

spare parts’ names, metrics like Levenshtein<br />

or Hamming distance are used and<br />

combined with triplets analysis (comparing<br />

all triplets-threes of characters found<br />

in all names in master data).<br />

The result are clusters of similar or duplicate<br />

items found in master data. These<br />

groups of highly similar items are given<br />

to maintenance engineers for validation.<br />

After duplicate items are confirmed, master<br />

data can be rectified – correct items is<br />

selected to be used and all duplicate items<br />

are erased or deactivated. Examples of<br />

Matching are presented in Figure 8 and<br />

Figure 9.<br />

Good spare parts management<br />

has significant impact and benefits<br />

It can be concluded that spare parts<br />

management as a part of physical asset<br />

management has significant impact on<br />

financial statement of the company. Good<br />

spare parts management brings the following<br />

benefits:<br />

• Optimum spare parts quantities are<br />

purchased<br />

• Optimal purchasing cashflow<br />

• Lower inventories<br />

• Less unused inventories<br />

• Higher availability of needed spare<br />

parts<br />

• Good risk management<br />

Various sets of data can be used to support<br />

or optimize spare parts management, especially<br />

in spare parts segmentation, criticality<br />

assessment, forecasting, master data<br />

rectification. The examples demonstrated<br />

in the paper indicate ways to utilize vast<br />

amounts of data available in organizations<br />

today – to monetize the data by improving<br />

efficiency and effectiveness of spare parts<br />

management processes.<br />


IAS 2 (2009) International Accounting Standard 16 – Inventories, IASCF 2009<br />

IAS 16 (2009) International Accounting Standard 16 – Property, Plant and Equipment, IASCF 2009<br />

IAS 16 (2009) Property, Plant and Equipment – Clarification on classification of servicing equipment as inventory or PP&E, IASCF 2009<br />

Hladík Tomáš, Tulach Petr: Are your spare parts really critical? Euromaintenance 2014, Helsinki Finland, 2014<br />

Nielsen Helms Erik: Impact on financial performance by physical asset management. The Asset Management Conference 2015, London, UK, 2015<br />

Tomáš Hladík, Petr Tulach, Eva Heringová: Financial impacts of spare parts inventory management – Finance driven spare parts inventory management. Euromaintenance 2016, Athens, Greece, 2016<br />

Willemain, T.R, Smart, C.N. and Schwarz, H.F.: A new approach to forecasting intermittent demand for service parts inventories. International Journal of Forecasting, 20, 375-387, ISSN: 0169-2070, 2004<br />

50 maintworld 2/<strong>2021</strong>









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