Maintworld 3/2017

In this issue: Using Technology and Innovation to Manage Mega-Maintenance Challenges Identify the Root Cause of a Misalignment Condition Elements of a Good Preventive Maintenance Program

In this issue:
Using Technology and Innovation to Manage Mega-Maintenance Challenges
Identify the Root Cause of a Misalignment Condition
Elements of a Good Preventive Maintenance Program


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3/<strong>2017</strong> www.maintworld.com<br />

maintenance & asset management<br />

Using Technology and<br />

Innovation to Manage<br />

Mega-Maintenance<br />

Challenges<br />

p 28<br />


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©2016 Moog Inc. All rights reserved.<br />


Dear friends,<br />

AS MOST OF YOU KNOW, the European Federation of National Maintenance<br />

Societies (EFNMS, www.efnms.org) is the umbrella organization for the nonprofit<br />

National Maintenance Societies in Europe. We would like to take this<br />

opportunity to review the EFNMS activities and cooperations.<br />

EFNMS is running several activities to provide added value to the members<br />

of the 24 National Maintenance Societies (its members): Workshops (in<br />

the topics of Benchmarking, Asset management, and Safety), Certifications<br />

(Maintenance Managers and Maintenance Technicians Specialists), Congress<br />

(EuroMaintenance, bi-annually), Surveys<br />

(in the topics of Maintenance KPIs and Asset<br />

management), Handbook (Global Maintenance<br />

and Reliability Indicators (GMARI), Harmonizing<br />

EN 15341 KPIs and SMRP metrics), and, recently,<br />

building a maintenance Body of Knowledge<br />

(BoK).<br />

EFNMS also has international cooperations in<br />

order to provide more added value: It is a member<br />

of the Global Forum on Maintenance and Asset<br />

Management (GFMAM, www.gfmam.org), having<br />

active participation in the development of its projects. In cooperation with<br />

the Salvetti Foundation, it is giving four maintenance awards (www.salvettifoundation.com/awards).<br />

Finally, EFNMS is a partner of the European Agency<br />

for Safety and Health at Work (OSHA, osha.europa.eu) and it is actively<br />

participating in its campaigns.<br />

More activities have been scheduled and results are soon expected (either<br />

within EFNMS or through the international cooperations) and the updates<br />

can be found on the official site. In parallel, Iceland and Hungary have joined<br />

EFNMS during <strong>2017</strong> and Romania is planning to join during 2018. In conclusion,<br />

a positive future for EFNMS is expected, to the benefit of everyone involved<br />

in the Maintenance field.<br />

We hope to see you all at the EFNMS activities and, even better, at the high<br />

quality EuroMaintenance2018 congress (www.euromaintenance2018.org) at<br />

Antwerp - Belgium, 25-28/09/2018!<br />

More activities have been scheduled and<br />

results are soon expected (either within EFNMS<br />

or through the international cooperations) and<br />

the updates can be found on the official site.<br />

Sincerely yours,<br />

Cosmas Vamvalis<br />

EFNMS Chairman<br />

48<br />

The<br />

benefits of<br />

vibration analysis are<br />

widely recognised<br />

in terms of reduced<br />

maintenance costs and<br />

the increased safety<br />

and plant efficiency it<br />

helps to provide.<br />

4 maintworld 3/<strong>2017</strong>

IN THIS ISSUE 3/<strong>2017</strong><br />

42<br />

Backlog management has<br />

a number of different but<br />

interdependent focuses:<br />

Backlog Work Order Quality,<br />

Age of Backlog and Backlog Size<br />

Management.<br />

34<br />

Benchmarking allows<br />

a company to compare<br />

its own practices<br />

and processes to the<br />

practices applied in<br />

the best firms of the<br />

industrial branch.<br />

6<br />

10<br />

Revolutionize Your Business<br />

with AI and Machine Learning:<br />

The Productivity Boost of the<br />

Century<br />

Developing Leadership in<br />

Maintenance and Reliability<br />

How to Identify the Root Cause<br />

14<br />

of a Misalignment Condition<br />

18<br />

Enjoy Success with Small<br />

Vibrometers<br />

IIoT Simplifies Predictive<br />

22<br />

Maintenance Solution<br />

Deployment and Maintenance<br />

Bearing Grease Replenishment -<br />

24<br />

On-Condition or Time-Based?<br />

Using Technology and<br />

28<br />

Innovation to Manage Mega-<br />

Maintenance Challenges<br />

Elements of a Good Preventive<br />

32<br />

Maintenance Program<br />

34<br />

38<br />

Demonstrating Value with<br />

Benchmarking<br />

What are you willing to do to<br />

improve reliability?<br />

Effective Backlog Management<br />

42<br />

Bearing Condition Monitoring<br />

44<br />

Using Ultrasound<br />

48<br />

Auto Correlation Simplifies<br />

Vibration Analysis, and<br />

Enhances Efficiency of Rotating<br />

Machinery Maintenance<br />

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

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

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

44 763 2573, ads@maintworld.com Subscriptions and Change of Address members toimisto@kunnossapito.fi, non-members<br />

tilaajapalvelu@media.fi Printed by Painotalo Plus Digital Oy, www.ppd.fi Frequency 4 issues per year, ISSN L 1798-7024, ISSN<br />

1798-7024 (print), ISSN 1799-8670 (online).<br />

3/<strong>2017</strong> maintworld 5


Machine learning has typically been linked with industries such as transportation<br />

and banking, but there are many uses for machine learning within the industrial<br />

sector. This article focuses on four industries within the industrial sector<br />

that are primed to take advantage of the application of machine learning and<br />

leverage the many benefits it can bring.<br />

The Productivity<br />

Boost of the<br />

Century<br />


Bentley Systems,<br />

richard.Irwin@<br />

bentley.com<br />

x 6 maintworld 3/<strong>2017</strong>


BEFORE STARTING, it is important to<br />

point out that there are many options<br />

and techniques available to gain more<br />

insight and make better decisions on the<br />

performance of your assets and operation.<br />

It all comes down to knowing what<br />

the best fit is for your needs and what<br />

type of data you are using.<br />

Machine learning makes complex<br />

processes and data easier to comprehend,<br />

and it is ideal for industries that<br />

are asset and data-rich. A great deal of<br />

data from various data sources are required<br />

in machine learning, and a data<br />

scientist or analyst may be needed to<br />







help set up and interpret the results.<br />

While it is possible to build your own ML<br />

platform, this design takes time, specific<br />

skills, and investment in a platform such<br />

as Microsoft Azure for a secure, private<br />

cloud platform for developers and data<br />

scientists. Alternatively, purchasing machine-learning<br />

capabilities off the shelf,<br />

as part of an asset performance management<br />

software solution, or outsourcing<br />

to a third party are options, provided you<br />

ensure input from in-house skills.<br />

Whatever path is chosen, the benefits<br />

machine learning can offer to big data<br />

are only just being brought to fruition.<br />

Opportunity is rapidly developing with<br />

productivity advancements at the heart<br />

of the data-rich industry in which you<br />

work. Here are some examples leading<br />

the way in this fast-moving digital transformation.<br />

Electric and Power<br />

We are all familiar with the term “smart<br />

grid” – the electrical supply network that<br />

utilizes digital technology and measures<br />

to detect and react to usage issues. In<br />

today’s turbulent times, electric utility<br />

companies are affected by ageing assets,<br />

increasing energy demand, and higher<br />

costs; the ability to recognize equipment<br />

failure and avoid unplanned downtime,<br />

repair costs, and potential environmental<br />

damage is critical to success across all<br />

areas of the business.<br />

Machine learning is augmenting the<br />

smart grid to better leverage and gain<br />

insight from the IoT with an enormous<br />

number of connected assets spread<br />

across a large network. Transformers,<br />

pylons, cables, turbines, storage units,<br />

and more — the potential for equipment<br />

failure is high and not without risk, so<br />

predicting failures with data and models<br />

is the new answer to keeping the network<br />

running smoothly. Another example<br />

of how machine learning helps the<br />

utilities industry is evidenced through<br />

demand forecasting, where predicting<br />

usage and consumption from numerous<br />

parameters can give a utility the advantage<br />

of being able to respond in advance,<br />

and balance supply with demand levels<br />

Smart meters can also be leveraged more<br />

individually so that customer recommendations<br />

regarding efficiency can<br />

be made. Machine learning also allows<br />

thermal images and video to be analyzed<br />

without the human eye to spot differences<br />

or anomalies in equipment. Additionally,<br />

asset health indexing can be<br />

leveraged to automate the analysis of extending<br />

asset life with machine learning,<br />

which is a low cost alternative to capital<br />

replacement.<br />

Oil and Gas<br />

In the oil and gas industry, the ability to<br />

recognize equipment failure and avoid<br />

unplanned downtime, repair costs, and<br />

potential environmental damage is<br />

critical to success across all areas of the<br />

business, from well reservoir identification<br />

and drilling strategy, to production<br />

and processing. In terms of maintaining<br />

reliable production, identifying equipment<br />

failures is one of the main areas<br />

where machine learning will play an important<br />

role. Predictive maintenance is<br />

the failure inspection strategy that uses<br />

data and models to predict when an asset<br />

or piece of equipment will fail so that<br />

maintenance can be planned well ahead<br />

of time to minimize disruption. With the<br />

combination of machine learning and<br />

maintenance applications leveraging<br />

IoT data to deliver more accurate estimates<br />

of equipment failure, the range<br />

of positive outcomes and reductions<br />

in downtime and the associated costs<br />

means that it is worth the investment.<br />

As well as predicative maintenance,<br />

the oil and gas industry has already started<br />

using machine learning capabilities<br />

in other areas. These include: reservoir<br />

modelling, where advanced analytics are<br />

used to make improved estimates on the<br />

properties of reservoirs based on historical<br />

data and models; video analysis<br />

that can be employed to detect patterns<br />

associated with anomaly detection; and<br />

case-based reasoning, which can help<br />

by siphoning out numerous parameters<br />

that account for well blow outs and leakages<br />

from a large example set of previous<br />

cases in order to come up with solutions.<br />

The application of machine learning has<br />

the potential to transform the oil and gas<br />

industry, which is even more crucial during<br />

the recent downturn in production<br />

and spending.<br />

Water Utilities<br />

Like the electric utilities mentioned previously,<br />

water companies also face the<br />

same challenges of an ageing infrastructure,<br />

rising costs, tighter regulations,<br />

and increasing demand. With that, they<br />

also share the same benefits that machine<br />

learning offers, such as identifying<br />

equipment failure before it happens —<br />

Common forms of<br />

machine learning<br />

techniques:<br />


using “trained” data<br />

• Linear Regression - Linear<br />

regression is used when data<br />

has a range, such as sensor<br />

or device driven data, and is<br />

used to estimate or predict a<br />

response from one or more continuous<br />

values<br />

• Classification – Classification is<br />

typically used for data that can<br />

be categorized, such as whether<br />

an email can be classified as<br />

genuine or spam.<br />


data without labelled responses<br />

• Clustering – The task of grouping<br />

a set of objects then deriving<br />

meanings from hidden<br />

patterns in the input data by<br />

putting the objects into similar<br />

groups.<br />

• Neural Networks – A rule-based<br />

computer system modelled on<br />

the human brain’s processing<br />

elements.<br />

3/<strong>2017</strong> maintworld 7


not just to predict a failure, but also to<br />

identify what type of failure will occur.<br />

Other benefits of machine learning in<br />

the water industry include meeting supply<br />

and demand with predictive forecasting<br />

and making smart meters “smarter”<br />

to help curb waste, such as during water<br />

shortages.<br />

Water distribution is another area<br />

that can be optimized with the application<br />

of artificial intelligence. Machine<br />

learning can be used in this scenario to<br />

speed up the decision-making process<br />

of how demand can be met by analyzing<br />

how much water needs to be supplied<br />

from the various locations (reservoirs,<br />

desalination plants, and rivers), as well<br />

as the pumping considerations and<br />

water movement, including associated<br />

costs and constraints. Machine learning<br />

will help determine the optimal low-cost<br />

methods of configuring network transfers,<br />

optimizing supply options, enhancing<br />

the raw water supply network, and<br />

determining the cheapest time to transfer<br />

water across the network.<br />

Flood detection can utilize machine<br />

learning by analyzing data from sensors,<br />

weather, geospatial location, alarms, and<br />

more to provide precise predictions and<br />

classifications of when and where floods<br />

are likely to occur at any given time;<br />

these predictions are based on current<br />

and historical data from all sources. This<br />

information would help utilities save<br />

time and costs, reduce false alarms, and<br />

lessen the impact on the environment.<br />

Manufacturing<br />

Manufacturing has always been the main<br />

industry when mentioned alongside<br />

machine learning, and for good reason, as<br />

the benefits are very real. These benefits<br />

include reductions in operating costs,<br />

improved reliability, and increased productivity<br />

— three goals that relate to the<br />

holy trinity of manufacturing. To achieve<br />

this, manufacturing also requires a digital<br />

platform to capture, store, and analyze<br />

data generated by control systems<br />

and sensors on equipment connected via<br />

the IoT. Preventative maintenance is key<br />

in improving uptime and productivity,<br />

so greater predictive accuracy of equipment<br />

failure is essential with increased<br />

demand. Furthermore, by knowing what<br />

is about to fail ahead of time, spare parts<br />

and inventory can use the data to ensure<br />

they align with the prediction. Improving<br />

production processes through a<br />

robust condition monitoring system can<br />

give unprecedented insight into overall<br />

equipment effectiveness by monitoring<br />

air and oil pressures and temperatures<br />

regularly and consistently. Other areas<br />




of use include quality control optimization<br />

to ensure quality is consistent<br />

throughout the manufacturing process.<br />

For example, adaptive algorithms can<br />

be used to inspect and classify defects in<br />

products on the production line with pattern<br />

recognition to reject defects, from<br />

damaged fruit to deformed packaging.<br />

Digitalization and<br />

transformation with<br />

machine learning<br />

Early adopters of machine learning<br />

are already reaping the benefits in the<br />

speed of information delivery, costs, and<br />

usefulness. As the technology advances,<br />

each industry is learning from each<br />

other, further advancing the use and<br />

influence of artificial intelligence. This<br />

gives you more information and insight<br />

to make smarter decisions. Bentley<br />

Systems’ users are combining machine<br />

learning with Bentley’s other digitalization<br />

technologies to make this process<br />

even more beneficial – by making it<br />

model-centric and adding visualization<br />

dashboards, cloud-based IoT data, analytics,<br />

and reality modelling to machine<br />

learning, the result is a complete solution<br />

for operations, maintenance, and<br />

engineering.<br />

Having a machine learning strategy<br />

in place will give you unprecedented<br />

insight into your operation and will lead<br />

to serious benefits in efficiency, safety,<br />

optimization, and decision making. The<br />

digital transformation for industry is<br />

now at a tipping point, with technologies<br />

all converging at the same time – a<br />

whole range of problems that once took<br />

months to address are now being resolved<br />

in a matter of minutes, all thanks<br />

to machine learning.<br />

8 maintworld 3/<strong>2017</strong>

APM Solutions to<br />

Keep You on Target<br />



Developing<br />

Leadership<br />

in Maintenance<br />

and Reliability<br />

Does your organization have what it takes to be successful in leading maintenance<br />

and reliability improvement across the facility and the corporation? Below, we will<br />

share with you the story of one young leader who has had the opportunity to<br />

lead two different organizations. Let us see what we can learn from the successes<br />

and the failures that could advance your maintenance and reliability efforts.<br />

10 maintworld 3/<strong>2017</strong>



Senior Instructor<br />

Eruditio, LLC,<br />

BLeahy@<br />

EruditioLLC.com<br />


CMRP CAMA, Partner<br />

Eruditio, LLC,<br />

SIsenhour@<br />

eruditiollc.com<br />

TWICE IN MY LIFE I have been charged<br />

with leading a group of 40 or so skilled<br />

workers. Once a success, and once was<br />

a glorious failure. In generalities, the<br />

situation was identical. A young leader,<br />

new to a profession, put in command<br />

of a highly skilled team with hundreds<br />

of years of collective experience. The<br />

expectation was that I had all the training,<br />

skills and natural ability to hit the<br />

ground running. As a Junior Army Officer,<br />

I had a good run as Platoon Leader. I<br />

affected change and together my platoon<br />

and I achieved our unit KPI’s. After a<br />

few more years of service I left the Army<br />

and took with me a high level of leadership<br />

confidence into the manufacturing<br />

world. I took on a cross-functional team<br />

of technicians and dreamt up a grand<br />

five-year Manufacturing and Reliability<br />

Improvement Plan for the site. Then I<br />

promptly crashed and burned. Despite<br />

past experience doing identical leadership<br />

tasks, I failed. To better understand<br />

the situation, I started asking what variables<br />

changed? This is what I discovered:<br />

I was spoiled in the Army. That system<br />

is established to ensure young leaders<br />

enjoy success despite themselves.<br />

Imagine a world where your boss, his<br />

boss, his boss, and his boss all held your<br />

job at some point in time. Even more,<br />

they had to be good at it to get promoted<br />

to positions of greater responsibility.<br />

Your senior employee is attached at your<br />

hip, trusts in and enforces your decisions,<br />

and never hesitates to provide<br />

necessary course corrections. There are<br />

many others just like you doing the exact<br />

same job. You are all friends and share<br />

best practices regularly. Everyone knows<br />

their job, has been trained to standard<br />

and is held accountable to it. Every procedure<br />

you need is published in easy to<br />

read manuals with pictures. And, there is<br />

a place called The Center for Army Lessons<br />

Learned that you can tap into. I realized<br />

it wasn’t so much me, but the expert<br />

support and training I received that<br />

was responsible for the success I had.<br />

Regretfully, this support system did<br />

not follow me into maintenance. No<br />

matter how many reliability engineering<br />

books I read, I could not prepare myself<br />

to stand alone in front of a maintenance<br />

team that had survived 12 maintenance<br />

bosses in as many years. The first year<br />

was exhausting and brutal. I acted alone<br />

while trying to reinvent the wheel. False<br />

starts and failed initiatives marked time.<br />

I quickly began questioning my competency<br />

and career path.<br />

What it boiled down to was, I needed<br />

the same sort of expert support I had<br />

in the Army. I needed: a coach to walk<br />

me through a Failure Mode Effects<br />

Analysis and Root Cause Analysis, a<br />

mentor to guide me through change<br />

management and work culture intricacies,<br />

peers to bounce ideas off regularly,<br />

and employees that were at a minimum<br />

aware of maintenance and reliability<br />

best practice, concepts, and language.<br />

I knew no one that had done the things<br />

I was reading about before. My boss<br />

hadn’t, I hadn’t, and my employees had<br />

never been exposed to it. So, I started at<br />

the top. I targeted the mill manager for<br />

sponsorship. As he became aware of reliability<br />

and maintenance improvement<br />

paybacks, resources became available.<br />

Technicians started attending training.<br />

I was provided opportunities outside of<br />

the company to develop my skill and become<br />

an expert in practical application.<br />

I also built my network of reliability and<br />







3/<strong>2017</strong> maintworld 11


maintenance peers within the company<br />

and beyond. It started slowly but by end<br />

of year two the Manufacturing and Reliability<br />

Plan revision meetings evolved<br />

from a one man show to a cross functional<br />

team of leaders, techs, and operators.<br />

The maintenance plan transformed<br />

from an individual business venture into<br />

a cooperative. A maintenance culture<br />

that valued training, partnership, and accountability<br />

began to take root.<br />

Five Points to Success<br />

The Army made it simple and to the<br />

point. I didn’t understand the advantages<br />

their system provided and why<br />

it worked at the time. It took a year of<br />

constant failure and much reflection to<br />

realize how and more importantly why<br />

they placed so much value on training,<br />

partnering and mentoring. Here are the<br />

five things I took away from the Army<br />

and built into my reliability improvement<br />

strategy.<br />

First, find experience. Sources of<br />

1| expert knowledge are available, if<br />

not in your company they are certainly<br />

available outside of it though organizations<br />

like EFNMS (European Federation<br />

of National Maintenance Societies) and<br />

SMRP (Society of Maintenance and Reliability<br />

Professionals). You need these<br />

experts to help paint the picture of what<br />

a good facility can look like. If you have<br />

never seen it, it can be hard to picture in<br />

your mind. Ask to visit world class sites<br />

or collect real world examples and case<br />

studies from successful sites. You need<br />

this experience and these examples to<br />

explain it to your organization.<br />

Second, we encourage young<br />

2| maintenance leaders to network<br />

as much as possible. Look for others that<br />

are experiencing the same things you are<br />

that you can talk to and compare notes.<br />

You can find them at conferences, local<br />

chapter events, and possibly even in your<br />

own organization.<br />

Third, find a mentor who will answer<br />

your questions and push you<br />

3|<br />

along. This is someone who will show<br />

you what to do differently and work with<br />

you when you get stuck using a tool or<br />

process.<br />

Fourth, constantly advocate for additional<br />

training, for you and your<br />

4|<br />

staff, from sources beyond the company.<br />

This outside information brings new<br />

perspectives and ideas to keep the organization<br />

moving forward.<br />

Fifth, document and share. Create<br />

5| standardized processes and tools<br />

where you can and roll them out across<br />

the group of young leaders to facilitate<br />

both onboarding, benchmarking, and<br />

understanding.<br />

Now if you are trying to create a reliability<br />

culture at the corporate level, I<br />

would ask are you providing for these<br />

needs and ensuring these new young<br />

leaders have the support they need from<br />

all levels or are you leaving them to reinvent<br />

the wheel in a vacuum?<br />

12 maintworld 3/<strong>2017</strong>

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Fig.1: Over 50% of machine failures are due to misalignment<br />

How to Identify<br />

the Root Cause of a<br />

Misalignment Condition<br />

GREG LEE,<br />

Senior Project<br />

Manager<br />


Inc. USA<br />

It is well known that misalignment is one of the greatest<br />

causes of failure in rotating equipment. In fact, research<br />

demonstrates that more than 50 percent of machine<br />

breakdowns are the direct result of poor alignment. But<br />

correcting misalignment can be a challenge.<br />

MISALIGNMENT CAN HAVE many causes.<br />

Alignment is not a static condition.<br />

Alignment changes when a machine<br />

warms up, its alignment can shift with<br />

the thermal expansion of its parts. When<br />

a machine vibrates, its skids can move<br />

and affect its alignment. When minor<br />

process parameters such as pressure or<br />

temperature are modified, alignment<br />

can change. Even the simple and natural<br />

succession of the seasons can alter alignment<br />

and put machine assets at risk.<br />

Effectively countering the factors that<br />

influence or alter alignment depends<br />

on understanding how the alignment<br />

14 maintworld 3/<strong>2017</strong><br />

of your machine changes over time and<br />

with use. It depends on accurate and<br />

careful analysis of the trends in your<br />

alignment data.<br />

It Can Be Difficult to Identify<br />

the Cause of Misalignment<br />

The root cause of a misalignment condition<br />

is not always obvious. Vibration<br />

analysis might uncover a misalignment<br />

problem, but it won’t necessarily identify<br />

the reason for it. Capturing alignment<br />

data before equipment is removed or disassembled,<br />

even when maintenance is<br />

undertaken for non-alignment reasons,<br />

may, over time, reveal hidden causes of<br />

misalignment. Periodically checking and<br />

recording alignment conditions generates<br />

useful information about correctable<br />

conditions that, if addressed, will<br />

reduce breakdowns, increase productivity,<br />

and save money.<br />

Maybe it’s a<br />

Foundation Problem<br />

Capture and analysis of alignment data<br />

trends proved useful at a co-generation<br />

plant in the San Francisco area. In this<br />

real life example, a plant operator found<br />

that his machines needed realignment


every six months. The requirement was<br />

always the same, the turbine needed to<br />

be shimmed up another 0.05-0.1 mm.<br />

By analyzing the alignment trends over<br />

time, it was discovered that the turbine<br />

foundation, built on fill dirt in an area of<br />

land recovered from San Francisco Bay,<br />

was slowly sinking. Vibration analysis had<br />

identified the misalignment problem, but<br />

only analysis of the gap and offset alignment<br />

trends revealed the reason why.<br />

Maybe it’s a Weather Problem<br />

Capture and analysis of alignment trends<br />

also assisted in correcting pump alignment<br />

problems in a high desert environment.<br />

In this case, a pump and pipe assembly,<br />

which had been properly installed<br />

and aligned, was inexplicably running in<br />

and out of alignment. Again, vibration<br />

analysis exposed the misalignment, but<br />

it was analysis of alignment trends that<br />

identified the source of the problem.<br />

Trend charts revealed that seasonal temperature<br />

extremes were negatively affecting<br />

alignment. In the western American<br />

desert, summer heat often runs above 110°<br />

F (43° C), while in the winter tempera-<br />

Fig. 2: Measurement results showing<br />

machine misalignment<br />

Fig. 3: Trend diagram showing seasonal temperature<br />

changes and impact on the alignment condition<br />

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tures sometimes fall below 0° F (-18° C).<br />

The reason for misalignment was easy to<br />

identify when examining the trends in<br />

the coupling clearances. As the outside<br />

temperatures changed with the seasons,<br />

a corresponding misalignment of the<br />

pump and pipe assembly became readily<br />

apparent.<br />

Monitoring Machine Health<br />

Knowing the condition of your machines<br />

can help avoid or mitigate costly<br />

breakdowns and failures. While keeping<br />

an eye on vibration levels is one wellestablished<br />

way to monitor the health of<br />

rotating machines, capture and analysis<br />

of actual alignment data will take your<br />

understanding and preparedness to the<br />

next level. Instead of merely signalling<br />

that a problem is already occurring,<br />

periodically checking the state of your<br />

alignment may allow you to anticipate<br />

or even avert the need for major repairs.<br />

Fully understanding how alignment data<br />

changes over time is central to maintaining<br />

operational readiness and effective<br />

alignment protection. When alignment<br />

data are collected and presented graphically<br />

in the form of trend lines or charts<br />

with specially designed software, exploring<br />

and understanding alignment data<br />

trends is easy. Alignment trend analysis<br />

is especially useful in identifying problems<br />

due to:<br />

Thermal Expansion – As machines<br />

warm up or cool down, alignment can<br />

change significantly. A “hot” alignment<br />

can help, but it will not capture all the<br />

elements of the changing alignment<br />

condition. Over time, machines that are<br />

not setup and adjusted to accommodate<br />

thermal expansion and contraction reveal<br />

dynamic misalignment problems by<br />

their high rates of failure.<br />

Seasonal Effects – Seasonal changes in<br />

temperature can dramatically alter the<br />

alignment of rotating equipment. Where<br />







Fig. 4: Vertical offset and gap indicating a constant settling of the<br />

turbine over the past 1.5 years<br />

pumps or exposed piping are located in<br />

an outdoor environment, the equipment<br />

is exceedingly vulnerable to significant<br />

seasonal effects and temperature extremes<br />

that impact alignment.<br />

Alteration in Process Parameters –<br />

Even small changes in the temperature,<br />

pressure, or other operating parameters<br />

can alter the dynamic forces and alignment<br />

of machines.<br />

Sub-Structures or Bases – Machine<br />

bases, skids, or plinths can move causing<br />

changes in alignment. Relocation<br />

of machines to operating facilities that<br />

have different substructures or different<br />

degrees of rigidity or flatness can negatively<br />

affect alignment.<br />

Uncertainty in Vibration Data –<br />

Sometimes vibration data does not<br />

clearly uncover a misalignment condition<br />

in time. Periodic measurement and<br />

analysis of alignment data can help identify<br />

all of these problems before critical<br />

failures occur. At the end of the day,<br />

timely information about actual alignment<br />

conditions will always be the best<br />

weapon in your arsenal to counter the<br />

forces that trigger alignment problems.<br />

Capture and analysis of alignment data<br />

trends will provide that information.<br />

16 maintworld 3/<strong>2017</strong>


Enjoy<br />

Success<br />

with<br />

Small<br />

Vibrometers<br />

How can a simple vibrometer successfully<br />

detect defects? This article contains<br />

a guide for reading the measurement<br />

results and how to reliably determine<br />

machine condition.<br />

EXPERIMENTAL ARTICLES on vibration<br />

diagnostics are almost always written<br />

about using a powerful analyzer. From<br />

my long experience in this area, I have<br />

only exceptionally met with a description<br />

of using a simple vibrometer. The<br />

usual opinion among maintenance people<br />

is that a powerful analyzer is needed<br />

for a real diagnosis; this is a myth. In my<br />

opinion even with a simple vibrometer,<br />

90 percent of defects can be accurately<br />

determined, and for the remaining 10<br />

percent it will at least point you in the<br />

right direction.<br />

A Simple Vibrometer -<br />

What Is It?<br />

A simple vibrometer must be able to carry<br />

out at least two basic measurements:<br />

- RMS vibration velocity measurement<br />

in the 10-1000 Hz band (referred<br />

18 maintworld 3/<strong>2017</strong><br />


SGLUNDA,<br />

Managing Director,<br />

Adash Ltd.,<br />

sglunda@adash.cz<br />

to as velocity in this article)<br />

- RMS vibration acceleration measurement<br />

in the 500-15000 Hz band (referred<br />

to as acceleration).<br />

If band frequency ranges are slightly<br />

different, this is not a defect. It is important<br />

that when measuring acceleration,<br />

speed frequency and its harmonics have<br />

been removed. The aim of measuring<br />

velocity is to detect mechanical defects<br />

such as an imbalance, misalignment,<br />

looseness and soft-foot. The purpose of<br />

acceleration is to determine the condition<br />

of the roller bearings and gears.<br />

If the vibrometer can also measure<br />

TRUE PEAK values, display time signal<br />

and evaluate the signal spectrum, then<br />

these measurement types will make the<br />

analysis even more reliable.<br />

The Basic Scheme<br />

of the Machine<br />

Simple machines have a drive part, usually<br />

an electric motor, and a driven part<br />

such as a fan, or pump. Both parts are<br />

usually connected by a shaft coupling<br />

and both shafts are mounted on rolling<br />

bearings. From now on we will refer to<br />

the driven part as “a fan”.<br />

A measurement point is a place on the<br />

machine where the vibration sensor is<br />

placed.<br />

There are standards that determine<br />

where the machine needs to be


measured, but we will not be dealing<br />

with them. They typically need many<br />

more measurement points than the<br />

five points that will be enough for our<br />

measurement. The machine has four<br />

roller bearings and we should select four<br />

measuring points as close as possible to<br />

these bearings. These four points must<br />

be radial, i.e. perpendicular to the shaft.<br />

Do not worry about whether to measure<br />

vertically or horizontally. You can<br />

choose any direction between these two<br />

directions. The last fifth point will be axial,<br />

i.e. parallel to the shaft. Put it on the<br />

coupling and it doesn’t matter whether<br />

it is on the engine or the fan. This fifth<br />

point is therefore perpendicular to the<br />

previous four.<br />

approximately +/- 5%. If the same test is<br />

carried out without a pad, the results will<br />

vary by +/- 50%. (See Figure 1)<br />

We deliberately did not mention<br />

measuring with a sensor that has no<br />

magnetic base, and that is just pushed<br />

onto the machine by hand. This method<br />

is unrepeatable. Unfortunately, it is<br />

sometimes used in maintenance and<br />

so the results are disappointing. Sometimes<br />

the whole vibration diagnostics<br />

programme is rejected, the only reason<br />

being unprepared measuring points.<br />












Once the measuring points have been<br />

chosen, they need to be prepared for<br />

the measurement. It is not possible to<br />

simply take the sensor with a magnetic<br />

base and put it on the uneven surface of<br />

the machine. Measuring pads must be<br />

stuck on the selected locations before<br />

measurements are taken. They have a<br />

flat surface. In addition, they guarantee<br />

that you will always measure at the same<br />

machine location. The basic rule for taking<br />

measurements is to make sure the<br />

measurement conditions are 100 percent<br />

repeatable. That is exactly what the<br />

measuring pads guarantee. Let’s try, for<br />

example, 10 repeated measurements in<br />

one place i.e. put the sensor on the pad,<br />

measure it and then remove it from the<br />

pad. You will find that the measurements<br />

are almost identical. They will vary by<br />

Figure 1. Measurement pad glued onto the<br />

machine surface (1), magnetic base (2),<br />

acceleration sensor (3).<br />

How to Find Warning and<br />

Alert Vibration Levels<br />

The first measurement has already been<br />

taken and the results obtained. But what<br />

do the numbers mean? Are the vibration<br />

readings low or high? With what should<br />

the results be compared? The easiest<br />

way is to use ISO 10816, but the limits<br />

given here have one significant defect.<br />

They apply to machines with speeds of<br />

600-3000 RPM. Let’s suppose the fan is<br />

unbalanced. The centrifugal force that<br />

causes vibration will vary significantly<br />

for 600 RPM and 3000 RPM. The dependence<br />

of the force on the speed is<br />

quadratic, i.e. 2x higher speed means 4x<br />

higher force. Therefore, the weight of<br />

the heavy point on the rotor may not create<br />

a problem at 600 RPM but will cause<br />

the fan to destruct at 3000 RPM. The<br />

warning and danger limit values should<br />

depend on the speed. (See Figure 2)<br />

If several similar machines are<br />

measured, then the situation is simpler<br />

because we can compare the values from<br />

all machines. If we get results equal to<br />

1.8, 2.1, 1.9 and 4.5 from the same point,<br />

then it is obvious that 2.0 means good<br />

machine condition. A machine condition<br />

with a value of 4.5 should be investigated<br />

further.<br />

The first step deals with regular<br />

measurements and monitoring the<br />

3/<strong>2017</strong> maintworld 19


Figure 2.<br />

vibration trend. If it is stable and has a<br />

permissible value, then the machine is in<br />

good condition and there is nothing else<br />

to do. If the value gradually increases and<br />

the warning threshold is exceeded, the<br />

second step of the evaluation must be<br />

carried out.<br />

The aim of the second step of the<br />

evaluation is to find the cause of the increased<br />

vibration. I will now describe the<br />

procedure of deeper analysis.<br />

Bearing Condition<br />

Needs Acceleration<br />


If the acceleration value has increased<br />

and the increase is only in one radial<br />

location, then it is easy. The problem is<br />

the poor condition of the roller bearing<br />

at this point. If the gears are measured,<br />

then the acceleration values can be increased<br />

in more places and it shows a<br />

problem with the gearing.<br />

Imbalance, misalignment and<br />

looseness need velocity<br />


The values are significantly increased on<br />

only one part (either the motor OR fan).<br />

If the increases in both radial directions<br />

are similar, then it is most probably an<br />

imbalance. If you have a signal spectrum<br />

at your disposal, you can find the significant<br />

value only on the speed frequency.<br />

If the increase differs significantly in<br />

both radial directions, or there is only a<br />

vertical increase, then it is most probably<br />

due to looseness. You should measure<br />

each machine foot. You would probably<br />

find significantly higher values on one of<br />

them.<br />

Electrical defect<br />

generates vibrations<br />


When the electric motor vibration looks<br />

like imbalance is the problem, then you<br />

should always also consider electrical<br />

defect. The electric motor may have<br />

winding defects, and despite this the<br />

vibration behaviour indicates an imbalance.<br />

Therefore a switch-off test on the<br />

motors should always be carried out.<br />

After switching off the power, one of two<br />

situations will occur.<br />

1) The velocity decreases slowly along<br />

with the rpm drop. This is a true imbalance.<br />

2) Immediately after switching off,<br />

the velocity increases for a very short<br />

time (1 sec), by a multiple and then drops<br />

to a very low value where it remains<br />

until the machine stops. This is an electromagnetic<br />

problem. The force field is<br />

not uniform and shifts the rotor off its<br />

mechanical centre of gravity. In the vibrations<br />

it will manifest as an imbalance.<br />

After switching off, the force is instantly<br />

lost and the rotor jumps back to the<br />

mechanical centre of gravity. This shock<br />

causes an increase in the value. Then the<br />

rotor starts spinning normally and the<br />

vibrations disappear.<br />

Mechanical imbalance<br />

Electrical fault<br />


If the velocity in the axial direction increases<br />

(usually it is higher than in the<br />

radial), always check the coupling and<br />

the alignment. It is misalignment that<br />

causes vibrations in the axial direction. If<br />

you have a spectrum, you can find higher<br />

values on the speed frequency and several<br />

harmonics.<br />


The velocity significantly increases on<br />

both parts (motor and fan) and only<br />

in the vertical directions. Take measurements<br />

across the frame below the<br />

machine. If there is a low value where<br />

the frame is supported, and high values<br />

between the supports then there is a<br />

resonance problem.<br />

A coast down measurement or<br />

gradual reduction of speed (frequency<br />

changer) will help. In case of resonance,<br />

the vibrations will decrease dramatically<br />

with a small speed change. If the<br />

standard operating speed cannot be<br />

changed, the frame must be additionally<br />

reinforced.<br />

Those who do Nothing,<br />

Make no Mistakes<br />

A lot of maintenance staff are unnecessarily<br />

nervous about carrying out vibration<br />

diagnostics. Simple devices are developed<br />

just for those who have no deep<br />

knowledge. If you take regular measurements,<br />

you will find that you have a<br />

much better overview of the condition<br />

of your machines. You will also certainly<br />

notice a decrease in the number of unexpected<br />

temporary shutdowns.<br />



20 maintworld 3/<strong>2017</strong>

The Uptimization Experts.<br />

What does<br />


mean to you?<br />



IIoT Simplifies<br />

Predictive Maintenance Solution<br />

Deployment and Maintenance<br />

There is a revolution happening. It is a slow burn right<br />

now but it is slowly gaining momentum throughout the<br />

world. It is the Industrial Internet of Thing (IIoT) revolution<br />

and it will change the IT manufacturing environment<br />

dramatically over the next 20 years.<br />


President of Beeond,<br />

Inc.,<br />

stan.brubaker@<br />

beeond.net<br />


real phenomenon that is being driven<br />

by standards organizations like OPC<br />

Foundation, OMAC and AMT. Additionally,<br />

Germany has a strategic initiative,<br />

known as INDUSTRIE 4.0, that is leading<br />

the European Union into the IIoT<br />

world so their manufacturing sector can<br />

remain globally competitive.<br />

So, why is there such excitement<br />

around IIoT and what do the OPC<br />

Foundation (opcfoundation.org) Organization<br />

for Machine Automation and<br />

Control (omac.org) and The Association<br />

for Manufacturing Technology (AM-<br />

TOnline.org) organizations have to do<br />

with it?<br />

The OPC Foundation has developed<br />

the OPC Unified Architecture (UA)<br />

Specification which enables IIoT in<br />

the manufacturing environment. UA<br />

enables information to be easily passed<br />

between sensors, machines, controls,<br />

monitoring devices and the cloud in a<br />

highly secure, flexible and open way with<br />

no custom integration code. The OMAC<br />

and AMT organizations, in partnership<br />

with the OPC Foundation, developed the<br />

Packaging Machine Language (PackML)<br />

and MTConnect version of the UA specification,<br />

respectively. It is the combination<br />

of OPC UA with these established<br />

industry standards that enables a much<br />

simpler and lower cost predictive maintenance<br />

solution.<br />

Users Spend an Inordinate<br />

Amount of Time and Money<br />

on Solution Integration and<br />

Maintenance<br />

The Manufacturing community (users)<br />

is dissatisfied with the amount of time,<br />

expense and complexity required to<br />

integrate and extract key metrics and<br />

Figure 1 - OPC UA / PackML Enabled Production Line<br />

Stan Brubaker and Beeond, Inc. provide IIoT<br />

consulting services with a focus on OPC UA<br />

adoption as an enabling technology. Stan<br />

has 20+ years in the product development<br />

and manufacturing execution systems (MES)<br />

business. This time includes 8 years managing<br />

software product development followed<br />

by 15 years managing large MES programs<br />

and projects and helping manufacturing<br />

companies realize business value from technology.<br />

Stan has a Bachelor of Science in<br />

Computer Science and an MBA from Penn<br />

State University and is a certified Project<br />

Management Professional (PMP).<br />

22 maintworld 3/<strong>2017</strong>


statuses between and from the machines<br />

they purchase from their suppliers and<br />

deploy in their plants. Each supplier has<br />

a different approach, naming convention,<br />

communication protocols, metrics<br />

and statuses. The complexity grows exponentially<br />

as more and more machines<br />

are added to the plant. There is very little<br />

standardization. This is all changing.<br />

The combined efforts of the OPC Foundation,<br />

OMAC and AMT have created a<br />

standard specification that when applied<br />

makes all machines look and act the<br />

same. What they actually do, e.g. washing,<br />

filling, capping, labelling, and casing<br />

and palletizing, are very different, but<br />

they are easily integrated to each other,<br />

to supervisory systems, and to the Cloud<br />

with little effort.<br />

Key Predictive Maintenance<br />

Metrics are in the Standard<br />

Both the OPC UA PackML and the OPC<br />

UA MTConnect specifications include<br />

standard machine states and maintenance<br />

metrics, e.g. uptime, downtime,<br />

reason codes, etc. so that all machines<br />

look and act the same. And OPC UA allows<br />

this information to be automatically<br />

accessed in a secure and reliable way.<br />

In UA terms, each machine is a Server of<br />

information and the plant’s Predictive<br />

Maintenance (PM) solution is both a<br />

Client or consumer of information and a<br />

Server providing information to supervisory<br />

systems like a plant HMI or Cloud<br />

solution (see the high-level architecture<br />

depicted in Figure 1).<br />

The power of OPC UA is that solutions<br />

like PM, when brought online, can<br />

automatically Discover the machines<br />

that are available in the plant and the<br />

machines can automatically serve the<br />

PM solution the information they have<br />

and if the machines are UA PackML or<br />

UA MTConnect enabled the semantics,<br />

naming conventions, states, etc. are all<br />

the same. The OPC UA PackML specification<br />

reduces the integration effort<br />

and complexity of machine to machine<br />

and the machine to supervisory solution<br />

information exchange.<br />

In addition to a simpler, lower cost<br />

PM implementation in a single plant,<br />

the OPC UA PackML and MTConnect<br />

standards enable a standard global view<br />

of performance and PM across all plants.<br />

Implementing an enterprise, global solution<br />

historically entailed very significant<br />

implementation, software, and hardware<br />

costs. In this enterprise scenario,<br />

OPC UA with PackML and MTConnect<br />

Beeond's 5 - Step<br />

IIoT Adoption Process<br />

• Faster Time to Market<br />

• Lower Risk and Cost<br />

Figure 2.<br />

1 Assessment<br />

Create an assessment<br />

Scorecard that maps<br />

your application against<br />

the OPC UA specification<br />

2 Roadmap<br />

Define a development<br />

roadmap of phased<br />

releases with work<br />

effort estimates<br />

3 Training<br />

Train developers on how<br />

to implement OPC UA<br />

solutions<br />

4 Development<br />

Develop software to<br />

implement OPC UA<br />

working with your<br />

development staff<br />

5 Compliance<br />

Assure that your product<br />

is compliant and can be<br />

logo certified by the OPC<br />

Foundation<br />

provide the capture and aggregation of<br />

data the plant level and the Cloud easily<br />

consumes, and presents that data. Adding<br />

to the lower cost, some Cloud technologies<br />

now provide a PM solution out<br />

of the box.<br />

Best Practice for Adoption<br />

of OPC UA, MTConnect and<br />

PackML<br />

As manufacturers and technology vendors<br />

put their IIoT and automation<br />

strategies in place, OPC UA must be a<br />

major component of their strategy. Because<br />

OPC UA is so comprehensive and<br />

all encompassing, moving to IIoT enabled<br />

automation means your strategy<br />

must address infrastructure, security,<br />

co-existence, migration and information<br />

model. How to start the adoption<br />

process can be overwhelming, but there<br />

is a practical, common sense approach to<br />

adoption.<br />

Beeond, Inc. (www.beeond.net) has<br />

defined a 5-Step Adoption Process that<br />

will accelerate your move to IIoT while<br />

reducing risk, cost and time to value. The<br />

5-Step Process is structured and organized,<br />

so users and vendors realize value<br />

quickly and cost-effectively.<br />

The 5-Step IIoT Adoption Process<br />

(Figure 2) will help both manufactures<br />

and technology vendors adopt the OPC<br />

UA Specification from concept to certification.<br />

The 5-Steps are:<br />

1. Assessment: Assessment of the company’s<br />

IIoT business, product and<br />

automation goals and requirement;<br />

the result produces an assessment<br />

scorecard that will map current capabilities<br />

and goals against the OPC UA<br />

specification.<br />

2. Roadmap: Defining a plan that addresses<br />

migration strategies, a prioritized<br />

roadmap of functionality<br />

defined in a phased release strategy<br />

and recommendations on tooling and<br />

training.<br />

3. Training: Training of development<br />

staff on how to implement the OPC<br />

UA specification using appropriate<br />

commercial SDKs.<br />

4. Development: Development of the information<br />

model and software modules<br />

needed for compliance, and<br />

5. Compliance: Assurance that product<br />

releases are compliance with the<br />

specification and that OPC UA Logo<br />

Certification, if desired, will be successfully<br />

achieved.<br />

The Benefits of Adoption<br />

Users and vendors who adopt OPC UA<br />

PackML or OPC UA MTConnect will be<br />

able to deploy their Predictive Maintenance<br />

solution faster and realize:<br />

• Lower integration complexity and<br />

cost<br />

• Lower solution maintenance<br />

• Consistent enterprise level view<br />

of performance and PM across all<br />

plants. The 5-Step Adoption Process<br />

will accelerate their move to IIoT using<br />

OPC UA so they realize benefits<br />

such as:<br />

• Understand the value and the return<br />

on investment that OPC UA can bring<br />

to your organization<br />

• Do an assessment and develop an<br />

IIoT realistic adoption roadmap for<br />

your organization<br />

• Experts’ guidance and a practical,<br />

common sense approach will reduce<br />

3/<strong>2017</strong> maintworld 23


Bearing Grease Replenishment -<br />

On-Condition or<br />

Time-Based?<br />


Director of Business<br />

Development for SDT<br />

International, allan@<br />

sdthearmore.com<br />

Maintaining plant assets at an optimal state of lubrication is a topic receiving lots of<br />

attention. Maintenance and Reliability practitioners dedicate teams to the task, but<br />

not every organization achieves world-class results.<br />

AS MUCH AS 80 PERCENT of all bearing<br />

failures are attributed to poor lubrication<br />

practices including:<br />

• Using the wrong lubricant<br />

• Lubricant deterioration<br />

• Lack of lubricant<br />

• Too much lubricant<br />

• Contamination<br />

• Mixing grease types<br />

• Using sealed bearings, but still providing<br />

a grease nipple access point on<br />

the motor<br />

Figure 1 - Collecting ultrasound<br />

data with SDT270 while<br />

replenishing lubricant.<br />

One glaring mistake that contributes<br />

to early bearing failure is over/under<br />

lubrication. Over and under lubrication<br />

is the product of scheduling grease<br />

replenishment on a time-based instead<br />

of a condition-based schedule, and not<br />

knowing how much grease to inject.<br />






Too Often - Too Late<br />

Too often bearings are being fed new<br />

grease before it is required. Other times<br />

the grease gun comes out too late.<br />

Some lubrication technicians guess<br />

at the quantity of grease to inject and<br />

do not even know how much grease is<br />

dispensed with a stroke of their grease<br />

gun. Bearing manufacturers provide<br />

formulae for calculating a theoretical<br />

grease capacity for each bearing, but not<br />

everyone knows how to use them. Still<br />

others simply follow guidelines given by<br />

the motor manufacturer. Often this “bad<br />

advice” is stamped directly on the motor.<br />

To drive home this point, Haris Trobradovic,<br />

one of SDT’s corporate trainers<br />

recently delivered training to a petrochemical<br />

facility in the Middle East.<br />

- During the training, we performed<br />

measurement practice on several machines<br />

(Figure 1). One of the machines<br />

was a fan, scheduled for re-lubrication a<br />

few days later, recalls Haris.<br />

- The customer’s standard greasing<br />

practice is to follow the manufacturer’s<br />

recommendations for both interval and<br />

amount. In other words, they grease on a<br />

time-based schedule and trust the motor<br />

manufacturer to guide on quantity.<br />

Trobradovic used the opportunity<br />

and performed re-greasing exactly as<br />

recommended by the OEM, even though<br />

the Condition Monitoring team had a<br />

different opinion. Their ultrasound data<br />

did not indicate any need for grease replenishment.<br />

The CM team members are<br />

strong advocates for on-condition lubrication<br />

and doing away with time-based.<br />

Following the facility’s lubrication<br />

Figure 2 - Two fan bearings with<br />

different load. Why do they share the<br />

same grease replenishment protocol?<br />

24 maintworld 3/<strong>2017</strong>


procedure raised several red flags. Figure<br />

2 shows two bearings driving the<br />

fan. Why would two identical bearings,<br />

but with different loads, have the exact<br />

same grease replenishment protocols?<br />

Maybe it is purely out of convenience;<br />

since the lubricator is there to grease the<br />

drive end bearing, a few strokes might<br />

just as well be pumped into the nondrive<br />

end at the same time.<br />

Another issue that disturbed the SDT<br />

Figure 3 - OEM instructs the owner to<br />

grease on a time-based schedule without<br />

considering the operating environment.<br />

trainer was the instructions stamped on<br />

the motor plate (Figure 3). This stamp<br />

instructs the owner of the motor to add<br />

32.7 grams of grease (grease type not<br />

identified) every 3,068 operating hours.<br />

Haris wondered if the OEM took into<br />

consideration the installation of the<br />

motor in a climate that is very hot and<br />

humid in the summer time, but cold,<br />

snowy, and dry in the winter.<br />

Don’t Mix Incompatible<br />

Grease Types<br />

One refreshing fact was an additional<br />

plate (Figure 4) with details about the<br />

grease type used in the bearing. Mixing<br />

incompatible grease types is an oftencited<br />

cause of premature bearing failure.<br />

This same reminder is provided by the<br />

SDT LUBExpert Ultrasound Tool. Prior<br />

to beginning a lubrication task LUBExpert<br />

reminds the operator of the correct<br />

grease type to use.<br />

Figure 4 - This motor had a secondary<br />

plate reminding lube-techs which grease<br />

type to use.<br />

Continuing with the experiment,<br />

Haris and the CM team attached the<br />

grease gun to the SDT equipment and<br />

greased the drive end bearing following<br />

OEM recommendations. Figure 5 is a<br />

screen shot captured from UAS, the companion<br />

software to LUBExpert. The top<br />

trend is the drive end bearing. Within<br />

four minutes the overall RMS increased<br />

by 7 dBµV while the Crest Factor and<br />

Peak spiked sharply.<br />

Figure 5 - Top trend graph illustrates drive<br />

end bearing after lubrication. It is badly<br />

over greased.<br />

The bottom trend is from the nondrive<br />

end bearing. Adding the requisite<br />

amount of grease had no positive outcome<br />

for the Overall RMS, which stayed<br />

stable at 26 dBµV. The drop in Crest Factor<br />

and Peak readings however, indicates<br />

the bearing may be entering a failed<br />

state. More frequent condition monitoring<br />

with complimentary technologies<br />

such as vibration analysis will ensure<br />

any machine downtime is scheduled on<br />

the client’s terms, not the machine’s.<br />

For those unfamiliar with these data<br />

formats, Overall RMS, Max RMS, Peak,<br />

and Crest Factor are unique condition<br />

indicators developed by SDT to bring<br />

analytical meaning to ultrasound STATIC<br />

data. Sadly, following OEM procedure<br />

resulted in over lubrication of the DE<br />

bearing.<br />

To drive home the point, Haris also<br />

captured DYNAMIC time signals from<br />

the drive end bearing. As seen in Figure<br />

6 the time signal before (bottom) and after<br />

(top) reveals new peaks and impacts<br />

Figure 6 - Dynamic data from drive end<br />

shows the emergence of defects (top) after<br />

bearing was over-greased following OEM<br />

recommendations<br />

forming. Over lubrication causes pressure<br />

to build inside the bearing. Ideally,<br />

the oil wants to feed from the thickener<br />

to form a thin film between the rolling<br />

elements and the race. It can’t do this if<br />

there is too much grease and pressure.<br />

The result is increased friction and impacting,<br />

two phenomena easily detected<br />

with ultrasound specialty tools like<br />

SDT’s LUBExpert.<br />

The Important Role of<br />

Lube-Techs<br />

Finally, Haris collected DYNAMIC time<br />

signals on the non-drive end bearing. In<br />

Figure 7 the bottom time signal shows<br />

dominant peaks that are clearly nonsinusoidal<br />

and indicative of impacting.<br />

After lubrication those peaks are gone.<br />

It appears that replenishing the grease<br />

in the non-drive end had some positive<br />

benefits, and those benefits are clearly<br />

illustrated in UAS time view.<br />

Figure 7 - The non-drive end bearing has<br />

defects as shown by this dynamic time<br />

signal. Ultrasound assisted lubrication with<br />

SDT allowed the CM team to identify this<br />

potential fault.<br />

The bottom line is that following OEM<br />

recommendations to replenish lubrication<br />

on a time-based or time-in-service<br />

protocol are proven wrong time and<br />

again. Following the greasing instructions<br />

stamped on the motor plate led to<br />

the drive end bearing being over greased<br />

and reducing life expectancy.<br />

Another interesting takeaway here<br />

is that, while an ultrasound lubrication<br />

solution – LUBExpert – was used<br />

to monitor the effects of adding grease,<br />

the added benefit for the CM team is<br />

the indication that a failure state may<br />

exist. There was a day not too long ago<br />

when the lubricator was counted on for<br />

keeping his finger on the pulse of the<br />

plant. Solutions like SDT’s LUBExpert<br />

are restoring important responsibilities<br />

to a task that recently has been given to<br />

“lower-skilled” tradespeople.<br />

It is past time that lube-techs be recognized<br />

for the important role they can<br />

play contributing to plant reliability.<br />

3/<strong>2017</strong> maintworld 25

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Using Technology and Innovation to<br />

Manage<br />

Mega-Maintenance<br />

Challenges<br />

Imagine this as your workweek<br />

routine: travel to<br />

work, grab your tools and<br />

climb 80 to 100 metres<br />

to your job site, on occasion<br />

your climb is 160<br />

meters. Sometimes, before<br />

you can make your climb,<br />

you take a boat or a helicopter<br />

to the job. That’s<br />

the life of someone who<br />

maintains onshore and<br />

offshore wind turbines.<br />

Although the renewable<br />

energy industry presents<br />

some extreme issues for<br />

maintenance and service,<br />

every industry has uptime<br />

challenges.<br />


Services Manager for<br />

Wind at Moog,<br />

jograbek@moog.com.<br />

MAINTENANCE MANAGERS in any industry<br />

are looking for service and repairs<br />

at increasingly faster turnaround times<br />

as well as less costly parts. Equipment<br />

makers have to figure out how to provide<br />

not only routine service at a faster<br />

pace but also handle priority requests<br />

without slowing down the rest of their<br />

service business, at the risk of delaying<br />

other customers’ repairs. The stakes are<br />

high on all sides. To illustrate that, let’s<br />

explore an example from the wind industry<br />

where we applied technology and<br />

service options for more reliability and<br />

cost savings.<br />

When a windfarm operator keeps a<br />

turbine free from unplanned maintenance<br />

and running at peak efficiency,<br />

it directly contributes to a company’s<br />

revenue and profit. If something does<br />

go wrong, every minute matters. When<br />

a wind turbine comes offline due to unplanned<br />

maintenance, the average daily<br />

cost to a windfarm is several thousand<br />

Euros.<br />

According to a 2011 ReliaWind research<br />

report, pitch system failures<br />

account for 23 percent of all downtime<br />

in wind turbines. This is more than<br />

any other component or system of the<br />

turbine. The ReliaWind report 1 goes on<br />

to note that pitch systems tallied the<br />

highest percentage of all component<br />

failures in wind turbines at more than 21<br />

percent.<br />

When compared to the size of a wind<br />

turbine, a pitch control system appears<br />

1 “Reliability-focused research on optimizing wind energy system design, operation<br />

and maintenance: Tools, proof of concepts, guidelines & methodologies for a new<br />

generation.”<br />

28 maintworld 3/<strong>2017</strong>


Inside a wind turbine<br />

hub, the pitch system<br />

adjusts the angle of<br />

the rotor blades.<br />




A worker exits a wind turbine’s nacelle<br />

and moves onto the hub to which each<br />

blade attaches.<br />

small. But pitch systems keep a turbine<br />

running and ensure the safety of the<br />

turbine in the event of high winds or<br />

catastrophic events. The pitch control<br />

system monitors and adjusts the inclination<br />

angle of the rotor blades and thus<br />

controls the speed of the rotor. Although<br />

these systems play an outsized role, they<br />

account for less than three percent of a<br />

wind farm’s capital expenses.<br />

I work for a global company, Moog<br />

Inc., which makes high-performance<br />

motion-control technology, including<br />

pitch systems. While we are always<br />

analyzing market trends and developing<br />

new solutions, our services business is<br />

where we reconnect with our customers,<br />

maintenance issues and technology,<br />

both legacy products and new ones.<br />

With the ReliaWind report above as<br />

context, we saw that windfarm operators<br />

were struggling with a variety of manufacturers’<br />

pitch systems. With a goal of<br />

alleviating maintenance and service issues<br />

as our focus, we set out to develop<br />

a new pitch system that required 50<br />

percent less maintenance than products<br />

already on the market made by other<br />

manufacturers and wind turbine designers.<br />

Our new system had fewer working<br />

parts, and we improved the design<br />

through using ultra-capacitors, instead<br />

of batteries, to eliminate backup power<br />

failures and periodic maintenance.<br />

By selecting AC synchronous motor<br />

technology (i.e., brushless, no fans for<br />

cooling), our engineers improved pitch<br />

system motor reliability and reduced periodic<br />

maintenance compared with the<br />

AC Induction or DC motors currently<br />

used by the wind turbine OEMs. These<br />

improvements are helping these new<br />

pitch systems increase reliability over<br />

existing industry designs by a remarkable<br />

223 percent.<br />

Case in Point<br />

Reducing the frequency and cost of<br />

maintenance takes a combination of<br />

new technology and creative options for<br />

service. For example, one of our pitch<br />

system clients had a maintenance issue<br />

in Brazil, a country with government<br />

regulations that can sometimes create<br />

challenges when procuring parts. Some<br />

of the wind turbines in Brazil are located<br />

in extremely remote locations. So our<br />

plan was to do everything possible to<br />

eliminate unscheduled service and be<br />

prepared should something happen. The<br />

customer simply couldn’t afford to wait<br />

weeks for new parts and repairs made<br />

from Europe. All parties concerned realized<br />

it was cheaper to scrap the parts in<br />

Brazil and send a part via a local, authorized<br />

supplier. Our local office in Brazil<br />

worked out a service agreement with our<br />

customer in Brazil to send rotable parts<br />

as clients needed them, and we, in turn,<br />

would keep an inventory of 15 to 30 core<br />

components of the pitch system for the<br />

customer to have on hand. As part of the<br />

plan, if we learned there was a critical<br />

volume of parts and repairs needed, we<br />

would organize a cost-efficient way of<br />

repairing the broken parts. We would do<br />

this by calling on either a localized partner<br />

or Moog technicians with technical<br />

assistance from our global support team.<br />

As our customer’s inventory of reworked,<br />

rotable parts is depleted and<br />

new requests come in for repairs, we<br />

have a trigger point at which we repair<br />

any damaged parts and return them to<br />

our original factory standard. The repairs<br />

are not immediately made to the<br />

client’s damaged parts; instead the client<br />

receives a refurbished, like-new item<br />

that our local supplier may have received<br />

weeks before from another customer. By<br />

eliminating the need to handle separate<br />

components inside each pitch system,<br />

3/<strong>2017</strong> maintworld 29


we have expedited service and enabled<br />

our clients to get their wind turbines<br />

back online much faster. This improves<br />

the Levelized Cost of Energy, or the net<br />

cost to install and operate a wind turbine<br />

against expected energy output over the<br />

course of the turbine’s lifetime (incentives<br />

excluded). And with rotable stock,<br />

we have enabled our customers in places<br />

like Brazil and elsewhere to reduce inventory.<br />

Tips and Strategies for Service<br />

Whether you are a maintenance manager<br />

relying on service or a manufacturer<br />

providing service, improving repairs<br />

takes flexibility. There has to be a willingness<br />

on all sides to think along new<br />

lines if you want to improve the way you<br />

deliver service and make repairs. Due<br />

to the challenges of wind turbine maintenance<br />

on a global basis, we analyzed<br />

ways we could better meet our wind energy<br />

customers’ expectations.<br />

We sat down with our customers to<br />

look for innovative ways to help them.<br />

In our case, we looked at the problem in<br />

two ways: First, how could we and our<br />

supplier solve the service problem in a<br />

way that best helped our customer? And,<br />

second, in what ways could we do this to<br />

ensure greater reliability of our systems<br />

and save maintenance costs.<br />

To help those customers not ready<br />

for an entirely new system, we are also<br />

providing retrofits using the ultra-capacitors<br />

and have seen vast improvements<br />

in less downtime due to backup failure as<br />

well as maintaining old battery systems.<br />

As a company we take what we learn on<br />

new systems and try to provide the same<br />

benefits for our retrofit customers.<br />

An additional service offering that<br />

Moog has made available to its clients is<br />

hands-on training on the exact system<br />

in the turbine, and on a scale that would<br />

truly make them capable of solving many<br />

of their own challenges and problems in<br />

the field without the need for Moog service<br />

personnel on-site. Our 800-squaremetre<br />

facility in Unna, Germany, provides<br />

technical training programmes to<br />

Moog’s global wind energy customers.<br />

At the centre, a team of expert trainers<br />

delivers customer training programmes<br />

ranging from a basic introduction to<br />

more advanced and focused engineering<br />

courses on products and systems. It is an<br />

investment that pays off for both our clients<br />

and Moog. A trained technician can<br />

diagnose, repair and restore a wind turbine<br />

to full operation in a fraction of the<br />

time it might take to remotely support<br />

an untrained technician. Overall, that<br />

will reduce the turbine’s downtime.<br />

In conjunction with the training centre,<br />

we also offer an around-the-clock<br />

help line. But even the help line is more<br />

efficient when the client placing the call<br />

has received a level of training that helps<br />

our services staff pinpoint a problem<br />

much faster.<br />

The training approach to service and<br />

maintenance has been so successful that<br />

we introduced a similar concept for our<br />

clients in China. We have been able to<br />

help our clients reduce downtime and<br />

the cost of energy by:<br />

• introducing new technologies like<br />

our latest pitch system, which is<br />

easier to maintain and can be monitored<br />

remotely;<br />

• adopting the concept of rotable stock<br />

and on-site support; and<br />

• providing quality training.<br />

And, ultimately, that spells an approach<br />

to maintenance and service that adds up<br />

to a reduced cost of producing energy for<br />

our customers.<br />





30 maintworld 3/<strong>2017</strong>

Be a LUBExpert<br />

®<br />


Poor greasing practices are<br />

a leading cause of bearing failure.<br />

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

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

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

engineered value.<br />

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

and when to stop.<br />

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

means longer life. LUBExpert alerts you when<br />

friction levels increase, guides you during<br />

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

lubrication.<br />

Grease Bearings Right<br />

Right Lubricant<br />

Right Location<br />

Right Interval<br />

Right Quantity<br />

Right Indicators<br />

Ultrasound Soluons<br />



Elements of a<br />

Good Preventive<br />

Maintenance<br />

Program<br />

Pages from<br />

CMS book.<br />

If your preventive maintenance program does not have the right content, it<br />

will never generate the desired and possible results. If you haven’t updated the<br />

program in the past five years, it probably contains not only too much PM but<br />

also the wrong activities. A good PM program has 90% of all PM activities done<br />

as inspections while equipment is running.<br />



Founder and CEO of<br />

IDCON INC., Raleigh<br />

NC, USA,<br />

info@idcon.com.<br />

CLASSICAL EXAMPLES of wrong and excessive<br />

PM are those activities on V-Belt<br />

drives, couplings and many other components<br />

with safety guards. Many PM<br />

programs suggest weekly inspections<br />

of these components by maintenance<br />

and at every shift by operators. On top<br />

of that, a shutdown PM is also done. The<br />

fact is that the design of most guards<br />

makes on-the-run inspection of the<br />

components impossible, and it doesn’t<br />

make sense to inspect something that<br />

cannot be seen.<br />

Many guards are big and heavy, so it<br />

can take two crafts people several hours<br />

to remove the guards, do the inspections<br />

and replace the guards during a shutdown.<br />

Even worse, if they find a problem<br />

on the component during the inspection<br />

and it has to be corrected before start up,<br />

this could lead to a prolonged shutdown<br />

and production losses.<br />

A correctly designed guard allows for<br />

inspections on the run (see Figure 1). In<br />

a route based inspection program, each<br />

of these inspections takes an average of<br />

three minutes including walking time. If<br />

a problem is found during these inspections,<br />

a planned and scheduled corrective<br />

maintenance action will be done<br />

when the opportunity presents itself.<br />

To decide the right content, you must<br />

understand three things:<br />

a. The consequence of component<br />

breakdown<br />

b. How failure can be detected<br />

c. How long before component breakdown<br />

can failure be detected<br />

Consequence of a Breakdown<br />

A breakdown is defined as the point in<br />

time when a component’s function ceases.<br />

The consequence of a breakdown can<br />

be prioritized in the following groups:<br />

a. Personal or environmental damage<br />

b. High costs for production lasses or<br />

Figure 1: The guard on the left is an<br />

example of a bad design for on-the run<br />

inspections. The one on the right is a good<br />

guard design for on-the-run inspections.<br />

32 maintworld 3/<strong>2017</strong>


maintenance to correct breakdown<br />

c. Preserve value<br />

As a first step, we advise not to go<br />

into any elaborate and time-consuming<br />

evaluation to find the criticality of equipment;<br />

this can be done later. We use<br />

the following fast approach to evaluate<br />

criticality:<br />

a. What will happen if this equipment<br />

breaks down? For 90% of equipment<br />

the answer is given by reading the<br />

nameplate of equipment and understanding<br />

the process. If there is spare<br />

equipment, you can find out how fast<br />

the spare equipment can be started<br />

b. Ask operators. If you do not know<br />

the answer to the first question, you<br />

should ask an operator. That should<br />

take care of another 50% of the remaining<br />

questions.<br />

c. Consult process and instrumentation<br />

drawings. It is bad if the operator does<br />

not know the answer, but it also identifies<br />

a need for training. Together,<br />

we will look at a process and instrumentation<br />

drawing to learn what<br />

will happen if the equipment breaks<br />

down. This will answer most of the<br />

unanswered questions.<br />

Using this screening process you only<br />

need to analyze what is important to analyze<br />

and you can save more than 90% of<br />

time as compared to processes suggested<br />

in Reliability Centered Maintenance and<br />

similar programs.<br />

Using the above approach, the next<br />

step will be to set up the right PM for<br />

each component (Coupling, valve, cooler,<br />

etc.) of the equipment (e.g. Hydraulic<br />

system).<br />

Documentation and Training<br />

After you have selected the right PM<br />

procedure, you need to document the<br />

procedure. It is important to decide on<br />

the document format, because it should<br />

be used to train people and improve the<br />

procedure in the future. Remember, in<br />

this case we are talking about basic inspection<br />

methods, not predictive maintenance<br />

(PdM) methods such as vibration<br />

analysis and wear particle analysis.<br />

It is easier AND safer to describe a<br />

method with pictures than words. The<br />

document also stands a better chance to<br />

be read and understood when it includes<br />

pictures. IDCON’s Condition Monitoring<br />

Standards books have 100 of the<br />

most common components documented<br />

in this type of format.<br />

At bare minimum, you need to include<br />

“what”, “how”, and especially<br />

Is it<br />

Practical?<br />

•<br />

NO<br />

Go to the next<br />

group<br />

YES<br />

NO<br />

“WHY” an inspection should be done.<br />

It does take time to create these documents,<br />

but once you do, the document<br />

can be re-used for most all components<br />

of the same type, for example a coupling.<br />

Frequencies and other values unique<br />

to the individual component will be<br />

described in the route list or in a hand<br />

held device. Do not make the mistake of<br />

assuming that crafts people or operators<br />

know how to inspect components.<br />

In our experience, crafts people have<br />

been trained to do repairs and trouble<br />

shoot existing problems. Very few have<br />

been trained in inspections to discover<br />

problems before they are actually problems.<br />

Much of this training is a thought<br />

process; you need to teach people to<br />

think about inspections and anticipate<br />

latent problems.<br />

At a minimum, training needs to<br />

include inspection methods for most<br />

common components and systems and<br />

a basic knowledge of instruments and<br />

tools such as high intensity lists, strobes,<br />

hand held IR instruments, optical tools<br />

and leak detectors.<br />

•<br />

Do they know YES<br />

• how?<br />

•<br />

•<br />

NO<br />

Can they be<br />

trained in > x<br />

minutes<br />

YES<br />

Implement<br />

task<br />

•<br />

Decision cycle<br />

Assign Resources<br />

It seldom works well to say, “PM is priority<br />

1 and we will assign different people<br />

to do it as we see the need.” Or worse<br />

still, “Our team decides who will do inspections<br />

today.” Trying to do it this way<br />

almost guarantees the PM effort will fail.<br />

Another common mistake is to assign<br />

the night shift to do PM when they have<br />

nothing else to do. The reason for having<br />

shift maintenance people is so they<br />

can respond to possible emergencies.<br />

If there are no emergencies, they are<br />

not needed on the shift and they can be<br />

moved to daytime work. The best results<br />

are always achieved when special people<br />

are assigned to do inspections on a full<br />

time basis.<br />

Assigning dedicated inspection resources<br />

garners the following:<br />

a. The right people to do the inspections,<br />

including in or adjustments and<br />

repairs<br />

b. The right people trained for this<br />

unique work<br />

c. The ownership and interest for PM<br />

that is necessary for continuously updating<br />

and improving PM work.<br />

d. An easier situation to manage. It can<br />

be very tempting to pull the people<br />

who are supposed to do PM to do<br />

emergency work.<br />

Wherever the assigned resources (PM<br />

inspectors) report to in your organizational<br />

structure, we advise they work<br />

very closely with the supervisor in the<br />

area where the inspections occur. They<br />

must report any findings and what they<br />

have inspected to the supervisor/area<br />

leader once or twice a day. When they<br />

have completed the route, they should<br />

do some of the repairs and adjustments<br />

that are the results of the inspections.<br />

This cuts back on administration and<br />

eases up the friction that can develop<br />

between PM inspectors and the crafts<br />

people who have to do all of the repairs.<br />

It is also important that PM inspectors<br />

start all routes with an interview<br />

with the operators in the area; this not<br />

only improves communication but also<br />

the on-the-job training of operators. The<br />

ultimate goal should be to have the operators<br />

do the majority of PM inspections.<br />

After you have decided the PM activity<br />

that needs to be done and the frequency<br />

you decide who should do it. The<br />

choices (in order of preference) are:<br />

a. Operator<br />

b. Area Maintenance- Mechanical, Electrical,<br />

Instrumentation crafts person<br />

c. In house expert, for example Vibration<br />

Analysis or Wear Particle Analysis<br />

d. Outside expert, for example X-ray,<br />

Acoustic Emission<br />

3/<strong>2017</strong> maintworld 33


Demonstrating<br />

Value with<br />

Benchmarking<br />

How can the service offering that creates the highest value to the customer be<br />

identified? How can industry-wide experience-based data and knowledge be exploited<br />

to provide, and continuously improve asset management services.<br />


VTT Technical Research<br />

Centre of Finland Ltd.,<br />

susanna.kunttu@vtt.fi<br />

HELENA<br />


VTT Technical Research<br />

Centre of Finland Ltd.,<br />

helena.kortelainen@vtt.fi<br />


Outotec Oyj, susanna.<br />

horn@outotec.com<br />

MANUFACTURING, mining and process<br />

industry companies around the world<br />

are looking for comprehensive solutions<br />

to raise and keep the overall equipment<br />

efficiency (OEE) at a high level. From<br />

the service provider’s point of view, this<br />

demand requires a deep understanding<br />

of the customer´s operation and<br />

maintenance processes, and of the<br />

various aspects affecting the business.<br />

In a global operation, service sites are<br />

seldom comparable: the installed base<br />

(fleet), environmental conditions, maintenance<br />

practices and processed raw<br />

materials can vary significantly - among<br />

other issues. Service companies focus<br />

on providing the customers with highest<br />

value services to improve their asset performance.<br />

Customer value is, however,<br />

34 maintworld 3/<strong>2017</strong><br />

case-specific. The solutions provided to<br />

one customer might not be as valuable to<br />

the next one due to e.g. customer-specific<br />

competences or external constraints.<br />

Benchmarking is a widely-used<br />

method that allows a company to compare<br />

its own practices and processes to<br />

the practices applied in the best firms of<br />

the industrial branch. A typical objective<br />

is to find justified development targets<br />

- and to benefit from existing good practices<br />

in the industry. Service providers<br />

could exploit benchmarking approaches<br />

together with their customers when<br />

looking for development needs in the<br />

asset management practices. Among<br />

many problems concerning benchmarking,<br />

one challenge is to make companies,<br />

plants or production lines and service<br />

site comparable.<br />

Benchmarking is not<br />

a single method<br />

The commonly applied benchmarking<br />

procedure has been the comparison of<br />

the average values of the particular industrial<br />

sector with the company’s own<br />

values (Komonen et. al 2011). In practice,<br />

benchmarking approaches make<br />

use of a variety of qualitative or quantitative<br />

methods. Qualitative methods are<br />

able to provide detailed and insightful<br />

benchmarking information if the number<br />

of involved companies is modest.<br />

Quantitative methods, in turn, provide a<br />

more efficient way to collect and analyze<br />

large data sets producing benchmarking<br />

information from a large number of<br />

companies. The benchmarking method<br />

and tool presented in this article is quantitative<br />

and requires data from several<br />

companies.<br />

Service provider can utilize<br />

benchmarking to develop<br />

customer service<br />

The benchmarking tool helps to identify<br />

and visualize potential sources of value.<br />

The benchmarking method promotes<br />

service providers’ ability to recognize<br />

improvement potential in customer’s<br />

asset management practices and the<br />

ability to find improvement actions for<br />

the current situation. The method for<br />

demonstrating value with benchmarking<br />

(Valkokari et. al. 2016) was developed in<br />

co-operation with Outotec that provides<br />

asset management services to the mining<br />

industry. The developed benchmarking<br />

approach is generic and applicable to<br />

other industries.<br />

The quality and plausibility of the<br />

data analysis results depends always on<br />

the quality of used data. Benchmarking<br />

methods make no exception. Benchmarking<br />

is typically used by an organisation<br />

that wants to compare own level of<br />

productivity or OEE, or some other key<br />

performance indicators with other companies<br />

in the same industry. If a service<br />

provider carries out the benchmarking,<br />

the potential customer may question<br />

the result due to possible commercial<br />

interests. Thus, transparency of the data<br />

collection and the data analysis is crucial<br />

for the credibility of the results. To make<br />

benchmarking transparent, the service<br />

provider and the customer should carry<br />

out the data collection and analysis in<br />

close cooperation. The common effort<br />

also provides a well-structured opportunity<br />

to discuss aspects related to e.g.<br />

the maintenance function and its successfulness.


Benchmark among similar<br />

companies<br />

In quantitative benchmarking methods,<br />

the basic assumption is that the companies<br />

are similar enough to be compared<br />

if they operate in the same industrial<br />

branch. In real life, the diversity of the<br />

companies can be extensive and poses<br />

a major drawback. If the benchmarked<br />

companies differ too much from each<br />

other, the benchmarked company is<br />

barely able to find the right development<br />

targets or even recognize the companies<br />

that should be a valid reference group.<br />

With the proposed approach based on<br />

categorizing sites to comparable units<br />

and benchmarking them against each<br />

other, the best practices will depend on<br />

the business environment. Thus, the<br />

first step is to recognize similar kinds of<br />

companies that can best learn from each<br />

other.<br />

In this context, similarity means that<br />

companies are comparable according to<br />

the aspects affecting asset performance<br />

and asset management practices. As the<br />

focus is in the development of maintenance<br />

service offerings, the benchmarking<br />

method categorizes sites or<br />

plants according to their maintenance<br />

environment. ”A maintenance environment”<br />

collects together the data arising<br />

from those sites that are similar enough<br />

with respect to external aspects affecting<br />

maintenance activities as illustrated<br />

in Figure 3. Maintenance environments<br />

describe features that affect the requirements<br />

for the maintenance function and<br />

include maintenance policy and maintenance<br />

activities. Features describing<br />

a maintenance environment include for<br />

example: availability of competent employees,<br />

climate effect on maintenance<br />

conduction, life cycle phase of equipment,<br />

maintainability of equipment, etc.<br />

From a service provider’s point of view<br />

these aspects are external and cannot be<br />

controlled by a service provider.<br />

Data collection<br />

The benchmarking method requires a<br />

quantitative data set that contains variables<br />

about the maintenance environment,<br />

applied maintenance practices<br />

and level of success. CMMS or other<br />

databases seldom contain such statistical<br />

data that is relevant from the benchmarking<br />

point of view. Thus, part of the<br />

method development was to establish a<br />

questionnaire for the data collection.<br />

Figure 1.<br />

Example of<br />

data collection<br />

questionnaire<br />

The questionnaire includes 34 questions<br />

that help to categorize the sites to<br />

different maintenance environments,<br />

recognize maintenance practices and<br />

calculate a key performance indicator<br />

to assess the successfulness of a site (see<br />

Figure 1). The service provider carries<br />

out the data collection in normal business<br />

negotiation situations. For this<br />

reason, the length of the questionnaire<br />

has to be reasonable and the questions<br />

should be easy to answer. The number of<br />

the questions is as small as possible and<br />

whenever possible the questionnaire<br />

offers ready alternatives. Pre-defined<br />

answer alternatives also support automated<br />

data analysis that allow discussion<br />

about results immediately after entering<br />

the data items.<br />

Figure 2.<br />

Main phases of<br />

benchmarking<br />

Figure 3. User<br />

interfaces of the<br />

benchmarking<br />

tool, which<br />

allow data<br />

collection and<br />

data analysis in<br />

a meeting with<br />

a customer<br />

Developing targets<br />

based on benchmarking<br />

Benchmarking is a tool to find out potentially<br />

weak points in the operation<br />

and offers an input for detailed discussion,<br />

and planning and prioritizing for<br />

development actions. In the developed<br />

benchmarking method a site under study<br />

is, based on the questionnaire entries,<br />

categorized to one of the pre-defined<br />

maintenance environments according to<br />

its similarity index value. The similarity<br />

index indicates the closeness of the site’s<br />

answers to the profile of a pre-defined<br />

environment. As illustrated in Figure 2,<br />

all sites belonging to the same maintenance<br />

environment are extracted from<br />

the benchmarking database for further<br />

analysis. The best sites of a particular<br />

36 maintworld 3/<strong>2017</strong>


maintenance environment are defined<br />

according to the values of key performance<br />

indicators, like availability or<br />

maintenance cost divided by equipment<br />

replacement value. Comparing maintenance<br />

practices between the benchmarked<br />

site and the best sites points out<br />

the differences in maintenance practices<br />

applied in operation and management.<br />

Investigating reasons and effects of<br />

these differences can reveal targets<br />

for development actions of the benchmarked<br />

site.<br />

Benefits from a service<br />

provider point of view<br />

Outotec Service Business Development<br />

is a function that has been actively looking<br />

for new ways of providing value<br />

to the customer. By developing the<br />

benchmarking concept in cooperation<br />

with different departments within the<br />

company as well as with certain customer<br />

sites, the development team has<br />

been able to structure the data gathering<br />

process. Moreover, it is able to better<br />

utilize installed base knowledge as well<br />

as understanding about the potential<br />

value sources for the customer, on a<br />

very concrete level. The benchmarking<br />

tool presented in Figure 3 can be used<br />

as a sales tool for the services business<br />

for an entire site or for sub-processes or<br />

process islands. It allows a value-based<br />

sales process, and more specifically, the<br />

matching of Outotec’s service offering<br />

against the customer’s actual needs, as<br />

defined on a detailed site assessment. It<br />

allows a transparent sales process, which<br />

can be defined in close cooperation with<br />

the customer. Outotec will be able to<br />

use the tool and its results also in internal<br />

product development, since it will<br />

become more aware of the customers’<br />

key challenges. Addressing the service<br />

product portfolio accordingly will give<br />

Outotec insight to what type of services<br />

the customers value the most.<br />

Summary<br />

There is a need for a systematic assessment<br />

framework for concretizing value,<br />

benchmarking it and ultimately optimizing<br />

the offered service solutions. The<br />

benchmarking method and tool helps<br />

to compare different sites according<br />

to their operational and maintenance<br />

environments. The benchmarking tool<br />

helps to identify and visualize the potential<br />

sources of value. With this approach<br />

based on categorizing sites to comparable<br />

units and benchmarking them<br />

against each other, the service provider<br />

is able to improve its capability in:<br />

• Showing improvement potential in<br />

asset management and make recommendations<br />

of applicable asset<br />

management policies,<br />

• Facilitating sales by optimizing the<br />

customer-specific product and service<br />

offering, and<br />

• Concretising customer value of the<br />

service provision.<br />

REFERENCES Komonen, Kari; Kunttu, Susanna;<br />

Ahonen, Toni (2011). In search of the Best<br />

Practices in Maintenance - New Methods and<br />

Research Results. Handbook 1 st International<br />

<strong>Maintworld</strong> Congress. Helsinki, 22-23.3.2011.<br />

KP-media Oy. Helsinki 2011, pp. 166-177.<br />

Valkokari, Pasi; Ahonen, Toni; Kunttu, Susanna;<br />

Horn, Susanna (2016). Fleet service solutions<br />

for optimal impact (in Finnish). Promaint –<br />

kunnossapidon erikoislehti. Kunnossapitoyhdistys<br />

Promaint ry, 30(1), pp. 36-39.<br />

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What are you<br />

willing to do to<br />

improve<br />

reliability?<br />

With strong leadership,<br />

reliability can be improved,<br />

and every employee will<br />

benefit. It is very difficult to<br />

force change, but when<br />

people are motivated<br />

anything is possible.<br />

Photo Steve Potts<br />


CMRP, Mobius<br />

Institute, jason@<br />

mobiusinstitute.com<br />

38 maintworld 3/<strong>2017</strong><br />


a very interesting discussion with an enthusiastic<br />

engineer who was frustrated<br />

with the progress made to improve reliability.<br />

He was primarily involved with<br />

condition monitoring, but he took every<br />

opportunity to talk to others about why<br />

equipment failed and what they could<br />

do to avoid failure. But it was rare that<br />

anyone took his advice. His supervisor<br />

was supportive, and would occasionally<br />

set up meetings with people to facilitate<br />

the discussion about reliability improvement.<br />

But again, very little actually<br />

changed…<br />

Does this seem familiar to you? Have<br />

you been trying to improve reliability<br />

but no one seems to take your advice?<br />

Do people agree that what you are suggesting<br />

makes perfect common sense<br />

but then go on doing what they have<br />

always done?<br />

I had two completely different suggestions<br />

for him. We need to create incentive<br />

and buy-in. I wonder if you have<br />

tried either of these. I would recommend<br />

trying both.<br />

Incentive: You need the<br />

support of senior management<br />

When suggestions for change come<br />

from a person who is at the same level of<br />

management, or below, (or from a different<br />

department), there is little incentive<br />

to change. Even if you believe that the<br />

proposed changes should be made, you<br />

are then left with extra work to do (or<br />

having to convince others to make the<br />

change), and justify the time that you<br />

spend pursuing those changes. But if<br />

that is not in your job description, if that<br />

is not how your performance is being<br />

measured, then it is unlikely you will<br />

spend any amount of time or effort on<br />

such a project.<br />

Therefore the directive needs to come<br />

from “above”. If a senior vice president,<br />

for example, made the declaration that<br />

reliability should be improved, and especially<br />

if people’s goals and job description<br />

changed as a result, then you will<br />

have a much greater chance of seeing<br />

change happen.<br />

Upon explaining this point I received<br />

the following response - a response I<br />

have heard many times before – “but I<br />

have explained the benefits of reliability<br />

improvement and made suggestions to<br />

quite senior managers, but they either<br />

nodded their head in agreement - and did<br />

nothing - or suggested I go and speak to<br />

someone else.”<br />

Ah yes, grasshopper, but how did you<br />

make your suggestion? (I didn’t really call<br />

him grasshopper.) The key is in the language<br />

you use and the detail you provide.<br />

How do you gain senior<br />

management support?<br />

As technical people we are often attracted<br />

to technical solutions. We can<br />

understand the logic. We especially like<br />

the common sense solutions. If we had

Successful reliability<br />

programs have one thing<br />

in common…<br />


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the time, we might be attracted by the<br />

prospect of solving a problem. However,<br />

for the most part, senior leaders are not<br />

technical people. They are not going to<br />

be involved directly the implementation<br />

of anything you suggest. They are instead<br />

motivated in the areas where their<br />

performance is measured; revenue, cost<br />

reduction, risk mitigation, regulatory<br />

compliance, customer satisfaction, delivering<br />

shareholder value (or whatever<br />

drives your business) and perhaps other<br />

things. Therefore, all they really want to<br />

hear is how you can help them achieve<br />

their goals.<br />

Therefore, rather than discussing<br />

technical issues, regardless of how practical<br />

and sensible they may seem to you,<br />

you need to focus on how reliability improvement<br />

helps them increase revenue,<br />

reduce cost, reduce risk, etc. You need to<br />





speak their language. You need to show<br />

how you can help them achieve their<br />

goals. And that’s how you will get their<br />

attention.<br />

There is a lot more that can be said<br />

about how you get their support, and<br />

that is for another article, but here is a<br />

summary:<br />

1. Understand what drives the business<br />

and how it measures success.<br />

2. Assess the risks faced by your organization<br />

(e.g. safety, environmental, production<br />

loss, etc.).<br />

3. Determine why and where the organization<br />

has poor reliability.<br />

4. Evaluate the extent to which reliability<br />

improvement can help close the gap<br />

between the current state and the desired<br />

state. Put a dollar/euro value on that gap.<br />

This is an investment after all.<br />

5. Evaluate the extent to which reliability<br />

improvement can help minimize the risks<br />

faced by your organization. If possible, put<br />

a dollar/euro value on the risk mitigation.<br />

6. Implement one or more pilot projects,<br />

and measure their effect, so that you can<br />

prove that it can work in your organization.<br />

7. Establish a business case that demonstrates<br />

the value of reliability improvement<br />

which is supported by the results achieved<br />

in your pilot projects. Don’t provide technical<br />

information unless requested. Make it<br />

clear how the goals of the senior management<br />

team can be achieved which includes<br />

mitigation of risk.<br />

Are you willing to do that?<br />

Upon making this suggestion I saw his face<br />

go pale. Senior management? Business case?<br />

Investment? Company goals? Pilot projects?<br />

It was a daunting prospect…<br />

Unfortunately, I was making life<br />

very difficult for him. I wanted to<br />

give him a simple solution - but over<br />

30 years of involvement with these<br />

programs, I have not seen anything<br />

else work. People need an incentive to<br />

change. Senior management is in the<br />

best position to create that incentive.<br />

Of course, they need to understand<br />

how each individual employee will<br />

benefit, but regardless, they need an<br />

incentive to do anything.<br />

Buy-in: Allow people to take<br />

ownership of the ideas<br />

If I came to you and suggested you<br />

do something differently, how will<br />

you react? Will you think that I am<br />

implying that you have been doing<br />

something wrong? Will you see it as<br />

more work you have to deal with? Will<br />

you have any buy-in, or ownership, of<br />

the process? What level of personal<br />

motivation will you have to make those<br />

changes; especially if you do not clearly<br />

understand the benefits?<br />

Sure, if I was your supervisor, and I<br />

required you to make a change you may<br />

make it, reluctantly. But unless I was<br />

very clear about the priorities, and I<br />

followed up with requests for progress<br />

reports, the change may not be made.<br />

What if we instead engaged in a<br />

conversation related to a goal you are<br />

trying to achieve or problem you are<br />

trying to solve? What if, during that<br />

discussion, you came to the conclusion<br />

that a change should be made?<br />

And what if you were in a position to<br />

take ownership of that change, and you<br />

knew that you would be recognized for<br />

taking the initiative and helping your<br />

coworkers and the business?<br />

Would you be more likely to make<br />

the change?<br />

If an organization sees the urgent<br />

need to improve reliability, and everyone<br />

has a thirst for making improvements,<br />

then suggestions from others<br />

are more likely to be taken on board<br />

and implemented. But if there is no<br />

push from above, and you do not see<br />

how you personally benefit, and there<br />

is no accountability, then there is very<br />

little reason to make any improvements<br />

whatsoever.<br />

Reliability improvement relies on<br />

developing a “culture of reliability” and<br />

engaging with people so that they actively<br />

contribute and benefit by the reliability<br />

improvement process. When it<br />

40 maintworld 3/<strong>2017</strong>


is a win-win, change will happen and<br />

the improvements will be sustained.<br />

If not, frustration will continue.<br />

Are you willing to do that?<br />

Upon making that suggestion I could<br />

again see some doubt. Technical<br />

people are not always great with the<br />

“touchy-feely” stuff. We may be interested<br />

in technical solutions, and<br />

we might want to dictate how things<br />

should be done and maybe even take<br />

credit for the change. That motivates<br />

you, but it doesn’t motivate the other<br />

person. So this is a tough decision to<br />

make. What’s most important, you<br />

or the people who are working with?<br />

What’s most important, you or the<br />

success of the reliability improvement<br />

program (i.e. the success of the<br />

organization)?<br />

Conclusion<br />

With strong leadership, reliability<br />

can be improved, and every employee<br />

will benefit. It is very difficult to force<br />

change, but when people are motivated<br />

anything is possible.<br />



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Backlog management has a<br />

number of different but interdependent<br />

focuses: Backlog<br />

Work Order Quality, Age of<br />

Backlog and Backlog Size Management.<br />

This article will focus<br />

on Backlog Work Order quality.<br />

Later editions of <strong>Maintworld</strong>magazine<br />

will cover Time in<br />

Backlog and Backlog Size Management<br />

in more detail.<br />

Effective<br />

Backlog<br />

Management<br />


Marshall Institute,<br />

sgiles@<br />

marshallinstitute.com<br />

WHILE A MAINTENANCE backlog is critical<br />

to an effective Planning and Scheduling<br />

process, it can be viewed negatively<br />

by some groups or individuals.<br />

In reactive organizations, putting<br />

a work order in backlog is viewed as a<br />

negative action. The assumption is made<br />

that the work order has been tossed<br />

into a black hole, never to return to the<br />

light of day. Unfortunately the nature<br />

of a reactive maintenance effort causes<br />

this belief to be correct: work orders<br />

only emerge from backlog when the<br />

condition the task is addressing has deteriorated<br />

to the point where it must be<br />

handled as an urgent or emergency work<br />

order. That, in turn, causes most work<br />

requests to be initially prioritized as<br />

urgent or emergency - piling more fuel<br />

on the fire-fighting nature of a reactive<br />

maintenance programme.<br />

In truth, the primary purpose of a<br />

maintenance backlog of the Planning<br />

and Scheduling process is to allow the<br />

maintenance planner adequate time<br />

to plan, order and receive materials<br />

and services before the task is placed<br />

on a schedule for execution. Without<br />

this window of time, most work will be<br />

scheduled before all the waste in the task<br />

has been removed, and in many cases<br />

before all the required material is on site.<br />

This results in an inefficient task at best,<br />

but also encourages the reuse of faulty<br />

parts resulting in a short-term repair.<br />

Early and accurate identification of<br />

maintenance tasks is key to providing<br />

this planning time. This early identification<br />

combined with realistic priority<br />

setting helps to bring credibility to the<br />

Planning and Scheduling process.<br />

A maintenance backlog is also used<br />

in some organizations to maintain a correctly-sized<br />

maintenance staff by crew.<br />

Backlog Work Order Quality<br />

The maintenance backlog has several<br />

different stages:<br />

Awaiting Planning – newly converted<br />

work requests awaiting the planning<br />

process<br />

In Planning – work orders the planner is<br />

actively planning<br />

Awaiting Materials or Services – planned<br />

and estimated work orders awaiting<br />

delivery of special ordered materials or<br />

for a specialty contractor to commit to a<br />

requested time of execution<br />

Ready to Schedule – work orders that are<br />

fully planned with all materials on the<br />

site and any contract or services needed,<br />

committed to the required timing<br />

Successfully managing the quality<br />

of work orders in the different stages of<br />

the backlog begins with the creation of<br />

the work request. A well-written work<br />

request will result in a well-written work<br />

order. A poorly-written work request will<br />

require someone (normally the planner)<br />

to investigate what the maintenance task<br />

really consists of before it can be converted<br />

to a work order for planning.<br />

Failure to address the quality of work<br />

requests entering the work order system<br />

will reduce the effectiveness of the planner<br />

and lead to at least some of the work<br />

orders not being adequately planned.<br />

Lack of a well-planned work order will<br />

reduce the accuracy of the estimates.<br />

Inaccurate work order estimates lead to<br />

over or understating the amount of real<br />

42 maintworld 3/<strong>2017</strong>


man-hours held in backlog. That inaccuracy<br />

will hinder the effective scheduling<br />

and the overall credibility of the entire<br />

planning and scheduling process.<br />

Shop Floor Training<br />

These problems can be addressed easily<br />

by providing shop floor training<br />

for everyone involved in creating the<br />

work request. Training combined with<br />

a clear setting of expectations by floor<br />

supervision and ongoing coaching by<br />

everyone involved in the work order<br />

process will lead to clear accurate work<br />

requests.<br />

In a world-class process, the requestor’s<br />

supervisor would be the first<br />

reviewer to quickly provide coaching on<br />

inadequate work requests. By addressing<br />

the inadequate work request at this<br />

point, not only will the work request be<br />

corrected at the source, but the clear<br />

expectation of creating a quality work<br />

request will be quickly established.<br />

The process flow shows the tight<br />

loop between the requestor and their<br />

supervisor, ensuring all information<br />

on the work request is adequate before<br />

sending the work request to the work<br />

request review meeting.<br />

A multifunctional team (primarily<br />

Maintenance and Operations supervision<br />

or management) reviews all open<br />

work requests prior to the start of the<br />

day. This is normally a very short meeting<br />

either approving the work request<br />

and sending it to the planner, or rejecting<br />

the work request.<br />

The process flow details the review<br />

process each work request is given before<br />

being submitted to the planner for<br />

conversion into a work order or deletion.<br />

(Some CCMS’s do not allow the work<br />

request to be deleted, but place a flag on<br />

the work request so that it cannot receive<br />

charges.)<br />

This review will minimize duplicate<br />

work requests and prevent the creation<br />

of duplicate work orders, incorrectly<br />

prioritized work requests, invalid work<br />

requests, and work requests assigned to<br />

the wrong queue.<br />

Monitor Open Work Requests<br />

A good metric to encourage an effective<br />

work request process is to monitor<br />

open work requests by age. Work<br />

requests should have a very short life<br />

– 24 hours to possibly 96 hours. Any<br />

work request older than 24 to 96 hours<br />

indicates a dysfunctional work request<br />

process.<br />

Work request training should be given<br />

to everyone in the organization expected<br />

to create work requests. World<br />

Class practices are to have everyone<br />

responsible for identifying and documenting<br />

maintenance tasks identified<br />

during their normal daily duties. The<br />

cost of CMMS access can sometimes<br />

cause this responsibility to be restricted<br />

to a smaller number of personnel.<br />

If that is the case, a companion system<br />

should be developed (hard copy or electronic)<br />

to expand the responsibility to<br />

identify maintenance tasks as broadly<br />

as possible.<br />

It is important that the work request<br />

training be formally documented to ensure<br />

quality work requests, regardless<br />

of personnel turnover.<br />

In parts 2 and 3, the Age of the Backlog<br />

and Backlog Size Management will<br />

be discussed in detail.<br />

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Figure 1.<br />

Bearing Condition Monitoring<br />

Using Ultrasound<br />

Airborne & structure-borne ultrasound has become a major player in bearing condition<br />

monitoring. Once considered just a leak detector, more maintenance & reliability<br />

professionals are beginning to realize all of the benefits associated with using ultrasound<br />

for condition monitoring applications. The P-F Curve, with which we have all<br />

become familiar, reflects that trend.<br />


CMRP, adrianm@<br />

uesystems.com<br />

THE I-P-F CURVE shows Ultrasound as<br />

being the first technology that detects a<br />

failure that is mechanical in nature such<br />

as early stage bearing wear, or subsurface<br />

bearing fatigue (See Figure 1.)<br />

It has been said that at least 60 percent<br />

of premature bearing failures can be attributed<br />

to lubrication, whether it’s over<br />

lubrication, under lubrication, use of<br />

the wrong grease for the wrong application,<br />

or use of a contaminated lubricant.<br />

Ultrasound instruments can be used<br />

to prevent over and under lubricated<br />

bearings. The source of ultrasonic noise<br />

is friction; when a bearing is in need of<br />

grease, there is an increase in friction and<br />

therefore an increase in noise or decibel<br />

level. When listening to the bearing that<br />

is in need of lubrication and watching the<br />

decibel level on the display of an ultrasonic<br />

instrument, as grease is applied the<br />

inspector would notice a gradual drop in<br />

the decibel level, eventually back down<br />

to a more normal level. If the bearing is<br />

already over lubricated, as soon as grease<br />

is applied, the inspector would notice a<br />

gradual increase in the decibel level, letting<br />

them know that the bearing already<br />

had enough grease.<br />

Figure 2. PUMP 3 MTROB 007 Figure 3. PUMP 4 MTROB 010<br />

44 maintworld 3/<strong>2017</strong>

3/<strong>2017</strong> maintworld x


How Do I Get Started?<br />

There are two common questions that<br />

many first-time users of ultrasound<br />

have. The first is, “How do I set baselines?”<br />

The second is, “How do I know if<br />

what I’m listening to is good or bad?”<br />

The Comparison Method<br />

One way to get a quick idea as to what is<br />

good and what is bad is by using the comparison<br />

approach. With this method, the<br />

inspector simply compares the decibel<br />

level readings at identical points on<br />

identical machines. Using this method,<br />

the inspector also begins to “train”<br />

their ear as to what rotating equipment<br />

sounds like, and it will become obvious<br />

that a bearing with a particular fault<br />

such as an inner race, or outer race defect,<br />

will sound much different than a<br />

bearing that is in a “good” condition.<br />

The baseline can then be set based on<br />

an average of decibel levels at the compared<br />

points. The software may even<br />

default to the first reading taken and<br />

downloaded. The baseline can then be<br />

changed as more readings are collected.<br />

The Historical Method<br />

The historical method is the preferred<br />

method for establishing baselines and<br />

alarm levels in bearing condition monitoring<br />

routes. Using this method, the inspector<br />

first establishes a route or database<br />

in the ultrasound software. The database<br />

is then loaded into the ultrasonic<br />

instrument. Data is then collected at the<br />

various points along the route. When the<br />

initial round of data has been collected,<br />

it may be necessary to collect data more<br />

frequently than needed in order to build<br />

Figure 4. Pump 4 MTR OB from the ultrasound instrument.<br />

Notice the distinct 175.8Hz harmonics detected.<br />

the history, and get an idea if the decibel<br />

readings are remaining similar in the<br />

historical readings.<br />

For example, when collecting the<br />

initial data for setting the baseline, the<br />

readings may need to be taken once per<br />

week for 4-5 weeks. Once the baseline is<br />

set, the readings need only be taken only<br />

once per month, or every other month<br />

depending on asset criticality and equipment<br />

runtime.<br />

Ultrasound Imaging<br />

Through advancements in ultrasound<br />

instruments and software, the user can<br />

obtain an “image” of the sound that is<br />

being heard to analyse, diagnose, and<br />

confirm mechanical fault conditions in<br />

rotating equipment.<br />

Examples of<br />

Ultrasound Imaging<br />

Let’s take a two motor and pump combination<br />

as an example: 60hp motors<br />

powering water pumps.<br />

While collecting data, both decibel<br />

readings and sound files were recorded.<br />

In Figures 1 and 2 screen shots from the<br />

spectral analysis software show a comparison<br />

between the points “PUMP 3<br />

MTROB 007” and the “PUMP 4 MTROB<br />

010.”<br />

Notice the difference between the<br />

two points. Both motors are operating<br />

under the same conditions, but the<br />

Pump 4 MTR OB point has a much different<br />

spectrum. If you were listening<br />

through the headset of the ultrasound<br />

instrument, it would also have a much<br />

different sound.<br />

Another image of the Pump 4<br />

MTROB point, captured from on board<br />

the ultrasound instrument, can be seen<br />

in Figure 3.<br />

The spectrum analysis software used<br />

has a built-in bearing fault frequency<br />

calculator. By entering in the speed<br />

(rpm) and the number of balls (bearings),<br />

an outer race, inner race, ball pass,<br />

and cage frequency are calculated. For<br />

this particular motor, the speed was<br />

1750rpm and the type and number of<br />









bearings was confirmed and the number<br />

of bearings was 10. The fault frequency<br />

calculated by the spectrum analysis software<br />

that was of interest was an inner<br />

race fault at 175Hz. This is the same fault<br />

harmonic detected on the ultrasound<br />

instrument. Another interesting point<br />

was the fact that the vibration analysis<br />

data was collected two days later, and did<br />

confirm an inner race fault on the Pump<br />

4 motor outboard point.<br />

Conclusion<br />

Implementing ultrasound for condition<br />

monitoring applications is easier<br />

than you think. With a short learning<br />

curve, ease of collecting data, and remote<br />

monitoring solutions, ultrasound can<br />

become another valuable tool to use for<br />

your condition monitoring efforts.<br />

Lubrication PM’s can also become<br />

more effective because ultrasound<br />

trends will show which bearings need<br />

to be lubricated. Therefore, instead of<br />

greasing everything on a time-based lube<br />

route, only the points that are currently<br />

in the lubrication alarm from ultrasound<br />

trends are greased until the decibel level<br />

drops back down to the baseline dB.<br />

If you are only using ultrasound as a<br />

leak detector, I would encourage you to<br />

take a more in depth look into condition<br />

monitoring with ultrasound.<br />

46 maintworld 3/<strong>2017</strong>

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Auto Correlation Simplifies<br />

Vibration Analysis, and Enhances<br />

Efficiency of Rotating<br />

Machinery Maintenance<br />

Vibration analysis is one of the most successful<br />

techniques for monitoring the condition of rotating<br />

equipment, but unless you are a vibration specialist<br />

the information can often be difficult to decipher.<br />

How can peak value analysis and auto correlation<br />

help improve maintenance efficiency?<br />


Emerson<br />

48 maintworld 3/<strong>2017</strong><br />

MISALIGNMENT, gear defects, insufficient<br />

lubrication, pump cavitation and rolling<br />

element bearing defects are all problems<br />

associated with rotating machinery that<br />

result in increased vibration. Vibration<br />

analysis is therefore one of the most<br />

important techniques for monitoring<br />

the condition of such machines as part<br />

of a predictive maintenance programme.<br />

The periodic and, where appropriate,<br />

continuous collection of vibration data<br />

enables potential problems to be identified<br />

earlier. This helps to prevent unexpected<br />

failures that can cause safety incidents<br />

and production loss. Maintenance<br />

can be scheduled at appropriate periods<br />

of downtime. The benefits of vibration<br />

analysis are widely recognised in terms<br />

of reduced maintenance costs and the<br />

increased safety and plant efficiency it<br />

helps to provide. However, with a shortage<br />

of experienced plant maintenance<br />

engineers, companies often do not have<br />

personnel with the necessary ability to<br />

correctly interpret the often-complex<br />

vibration data available.<br />

Vibration analysis relies on data collected<br />

from vibration sensors monitoring<br />

the rotating equipment. This data<br />

can be collected manually and periodically<br />

using handheld vibration analysis<br />

devices. Alternatively, equipment critical<br />

to production is often monitored<br />

on a continuous basis (often referred<br />

to as online monitoring) to ensure that<br />

changes that may indicate a potential<br />

problem are not missed in between manual<br />

rounds. Online monitoring systems<br />

also often incorporate protection functionality<br />

that helps to bring equipment<br />

to a safe state (offline) should an issue be<br />

identified.<br />

Signal processing<br />

In general, the analogue signal from a vibration<br />

sensor is routed via an analogue<br />

signal processor, converted into a digital<br />

format and then further processed digitally.<br />

The output of the vibration sensor<br />

is expressed in g units, and the signal<br />

processing may include the conversion<br />

of the signal to velocity units. The<br />

analogue signal (in g or velocity units) is<br />

usually passed through a filter immediately<br />

before being converted into a digital<br />

format, providing assurance that the<br />

digital representation of the analogue<br />

signal is correct.<br />

By far the most common form of signal<br />

processing for analysing vibration<br />

from rotating equipment is the Fourier<br />

Transform. This uses a fast Fourier<br />

transform (FFT) algorithm to enable the<br />

signal to be converted and to construct<br />

the spectrum either in acceleration or<br />

velocity units. This spectral analysis is<br />

helpful in separating the band-limited<br />

signal into periodic components related<br />

to the turning speed of the machine.<br />

Standard spectral analysis is the traditional<br />

method used to gain insight into<br />

machinery problems that create vibration,<br />

but its complexity makes it difficult<br />

for anyone who is not a specialist vibration<br />

engineer to analyse and interpret<br />

the data. In contrast, the peak value<br />

analysis (PeakVue) methodology introduced<br />

by Emerson to help analyse vibration<br />

data has proven to be very effective,<br />

presenting the information in a way that<br />

makes it easier for personnel other than






vibration specialists to interpret and<br />

identify problems.<br />

Peak value analysis<br />

Peak value analysis technology provides<br />

a simple, reliable indication of equipment<br />

health via a single trend - filtering<br />

out traditional vibration signals to focus<br />

exclusively on impacting faults, where<br />

metal parts come into contact with each<br />

other.<br />

In this method, peak values are observed<br />

over sequential discrete time intervals,<br />

captured, and then analysed. The<br />

analyses are:<br />

A. the peak values (measured in g’s).<br />

B. spectra computed from the peak value<br />

time waveform.<br />

C. the auto correlation coefficient computed<br />

from the peak value time waveform.<br />

All three analysis tools enable the<br />

defect, and often its severity, to be identified.<br />

As a measure of impacting, peak<br />

value analysis readings are much easier<br />

to interpret. A healthy machine that is<br />

correctly installed and well lubricated<br />

shouldn’t have any impacting. This establishes<br />

the zero principle: the peak value<br />

measurement on a healthy machine<br />

should be at, or close to, zero.<br />

As common machinery faults begin to<br />

appear on rotating equipment, the peak<br />

value reading typically can be evaluated<br />

using the so-called Rule of 10’s.<br />

This applies to rolling element bearing<br />

machines operating between 1000 and<br />

4000 rpm. It simply states that when the<br />

peak value levels reach 10, there is some<br />

problem with the machine; when they<br />

double to 20 there is a serious problem;<br />

and when they double again to 40 there<br />

is a critical problem (see Figure 1).<br />

Rule of 10’s example<br />

As an example of how the Rule of 10’s<br />

operates, let’s consider a typical process<br />

pump running at between 900 and 4000<br />

rpm as it passes through the four stages<br />

of bearing failure before progressing to<br />

machine failure.<br />

STAGE 1 -The defect is not visible to the<br />

human eye and there is no change in the<br />

overall vibration, but peak value analysis<br />

already provides an indication that<br />

something is happening. When the peak<br />

value rises to a value of 10, this indicates<br />

that there is a problem with the bearing.<br />

STAGE 2 - Small pits begin to appear<br />

and the bearing has less than 10% of its<br />

service life remaining. Typically, overall<br />

vibration still does not provide an<br />

indication of the developing faults, but<br />

the peak value level continues to climb.<br />

When it doubles to 20, this indicates a<br />

serious problem with the bearing.<br />

STAGE 3 - the bearing damage is now<br />

clearly visible. You may start to see a<br />

small increase in overall vibration of +/-<br />

10 percent. Meanwhile, the progression<br />

in fault severity is obvious using peak<br />

value analysis.<br />

STAGE 4 - the overall vibration may rise<br />

by 20 percent or more. In comparison,<br />

the peak value level continues to increase<br />

sharply – perhaps as high as 40<br />

g’s – and signals that the bearing is approaching<br />

the end of its life.<br />

MACHINE FAILURE - there will be a<br />

marked increase in the overall vibration<br />

at the point of actual failure, but too late<br />

Figure 1. Operators with no special<br />

training in machinery diagnostics can use<br />

peak value analysis measurements to<br />

determine both when a piece of rotating<br />

equipment is healthy and when an<br />

abnormal situation is present.<br />

to support planned maintenance. This is,<br />

in effect, notification that the machine<br />

is shutting down. In contrast, peak value<br />

analysis has been indicating a developing<br />

fault over the past weeks and months.<br />

Immediately prior to failure, peak value<br />

levels may surge rapidly to 50 g’s or<br />

higher.<br />

Operators with no special training in<br />

machinery diagnostics can use peak value<br />

analysis measurements quickly and<br />

easily to determine both when a piece of<br />

rotating equipment is healthy and when<br />

an abnormal situation is present. Once<br />

an abnormal situation has been identified,<br />

detailed diagnostic information<br />

can be extracted from the peak value<br />

analysis waveform or spectrum to determine<br />

the exact nature of the defect. This<br />

method can be used to visualise distress<br />

signals on a machine that are simply not<br />

visible with other vibration measurements.<br />

Earlier indication of developing<br />

defects facilitates optimum maintenance<br />

planning and minimises the impact on<br />

production.<br />

Auto correlation<br />

Auto correlation is a time domain analysis,<br />

computed from the peak value time<br />

waveform that is useful for determining<br />

the periodicity or repeating patterns of<br />

a vibration signal. The auto correlated<br />

waveform can be presented in a circular<br />

format, which makes interpretation of<br />

the data much more straightforward.<br />

On the following page are some examples<br />

of how vibration analysis data<br />

can be viewed, using standard spectrum<br />

analysis, time waveform, auto correlation,<br />

and finally auto correlation in a<br />

circular format.<br />

Conclusion<br />

Peak value methodology has proven to<br />

be a very useful tool for vibration analysis<br />

in rotating equipment applications<br />

where normal spectral analysis has<br />

proven to be less effective. Using auto<br />

correlation and circular displays, problems<br />

can be easily identified without<br />

vibration analysis experience. This helps<br />

to simplify maintenance tasks, enabling<br />

a greater number of devices to be effectively<br />

monitored. Previously difficult to<br />

identify problems will be quick and simple<br />

to diagnose at an early stage, helping<br />

repair work to be scheduled, preventing<br />

machinery failures, reducing overall<br />

maintenance costs and improving plant<br />

safety and efficiency.<br />

3/<strong>2017</strong> maintworld 49





When using standard vibration<br />

monitoring to monitor problematic<br />

gearboxes, the levels in the vibration<br />

spectrum do not appear to be that high<br />

- 0.5 mm/sec. Looking at the spectrum<br />

you can see increasing harmonics of<br />

the suspected gear mesh frequency.<br />

To trained personnel, the gear shows<br />

potential signs of misalignment. However,<br />

to anyone who is not a vibration engineer,<br />

this means very little and can be difficult<br />

to analyse.<br />

Using the time domain, the difference in activity levels is more apparent. The high levels<br />

and activity in the top waveform is a huge contrast to the lower time waveform. Modulation<br />

can be seen in the top waveform, but to anyone other than a vibration engineer, this doesn’t<br />

mean a great deal. Note that the scales on both plots are the same at 18g’s.<br />


Comparison gearbox<br />

Noisy gearbox<br />

When the time signal is auto correlated, it produces a value of between 0 and 1. When it<br />

is close to 0 it means the signal consists of random frequencies, which indicates a lack of<br />

lubrication. When close to 1, that shows that there are repeatable signals, such as impacting,<br />

indicating a broken gear tooth or a failing bearing. You can see that there is modulation in<br />

the signal on the noisy gearbox and the readings are close to 1. Even to a layman, diagnosis<br />

of the gearbox is easy.<br />

Top: healthy gearbox clearly showing<br />

lack of gear mesh frequencies. Bottom:<br />

problematic gearbox showing harmonics<br />

of the gear mesh frequency. This diagram<br />

compares vibration readings that were<br />

taken on two gearboxes – one healthy and<br />

one problematic. Note the lack of gear<br />

mesh frequencies in the healthy gearbox<br />

compared to the other.<br />

Comparison gearbox<br />

Noisy gearbox<br />

By showing the auto correlated waveform in a circular format, the difference becomes<br />

obvious and the misalignment of the gear can be clearly seen. In this case, the misalignment<br />

was due to mismatched gears.<br />

50 maintworld 3/<strong>2017</strong>

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Made in Germany<br />

Global Presence<br />

Qualified Support<br />

Quality Service<br />

PRÜFTECHNIK Dieter Busch AG<br />

Oskar-Messter-Str. 19-21<br />

85737 Ismaning<br />

Tel.: +49 89 99616-0<br />

info@pruftechnik.com<br />


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