Maintworld 3/2017

3/2017 www.maintworld.com 

maintenance & asset management 

Using Technology and 

Innovation to Manage 

Mega-Maintenance 

Challenges 

p 28 

IDENTIFY THE ROOT CAUSE OF A MISALIGNMENT CONDITION P 14 ELEMENTS OF A GOOD PREVENTIVE MAINTENANCE PROGRAM P 32

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Dear friends, 

AS MOST OF YOU KNOW, the European Federation of National Maintenance 

Societies (EFNMS, www.efnms.org) is the umbrella organization for the nonprofit 

National Maintenance Societies in Europe. We would like to take this 

opportunity to review the EFNMS activities and cooperations. 

EFNMS is running several activities to provide added value to the members 

of the 24 National Maintenance Societies (its members): Workshops (in 

the topics of Benchmarking, Asset management, and Safety), Certifications 

(Maintenance Managers and Maintenance Technicians Specialists), Congress 

(EuroMaintenance, bi-annually), Surveys 

(in the topics of Maintenance KPIs and Asset 

management), Handbook (Global Maintenance 

and Reliability Indicators (GMARI), Harmonizing 

EN 15341 KPIs and SMRP metrics), and, recently, 

building a maintenance Body of Knowledge 

(BoK). 

EFNMS also has international cooperations in 

order to provide more added value: It is a member 

of the Global Forum on Maintenance and Asset 

Management (GFMAM, www.gfmam.org), having 

active participation in the development of its projects. In cooperation with 

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

Finally, EFNMS is a partner of the European Agency 

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

participating in its campaigns. 

More activities have been scheduled and results are soon expected (either 

within EFNMS or through the international cooperations) and the updates 

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

EFNMS during 2017 and Romania is planning to join during 2018. In conclusion, 

a positive future for EFNMS is expected, to the benefit of everyone involved 

in the Maintenance field. 

We hope to see you all at the EFNMS activities and, even better, at the high 

quality EuroMaintenance2018 congress (www.euromaintenance2018.org) at 

Antwerp - Belgium, 25-28/09/2018! 

More activities have been scheduled and 

results are soon expected (either within EFNMS 

or through the international cooperations) and 

the updates can be found on the official site. 

Sincerely yours, 

Cosmas Vamvalis 

EFNMS Chairman 

48 

The 

benefits of 

vibration analysis are 

widely recognised 

in terms of reduced 

maintenance costs and 

the increased safety 

and plant efficiency it 

helps to provide. 

4 maintworld 3/2017

IN THIS ISSUE 3/2017 

42 

Backlog management has 

a number of different but 

interdependent focuses: 

Backlog Work Order Quality, 

Age of Backlog and Backlog Size 

Management. 

34 

Benchmarking allows 

a company to compare 

its own practices 

and processes to the 

practices applied in 

the best firms of the 

industrial branch. 

6 

10 

Revolutionize Your Business 

with AI and Machine Learning: 

The Productivity Boost of the 

Century 

Developing Leadership in 

Maintenance and Reliability 

How to Identify the Root Cause 

14 

of a Misalignment Condition 

18 

Enjoy Success with Small 

Vibrometers 

IIoT Simplifies Predictive 

22 

Maintenance Solution 

Deployment and Maintenance 

Bearing Grease Replenishment - 

24 

On-Condition or Time-Based? 

Using Technology and 

28 

Innovation to Manage Mega- 

Maintenance Challenges 

Elements of a Good Preventive 

32 

Maintenance Program 

34 

38 

Demonstrating Value with 

Benchmarking 

What are you willing to do to 

improve reliability? 

Effective Backlog Management 

42 

Bearing Condition Monitoring 

44 

Using Ultrasound 

48 

Auto Correlation Simplifies 

Vibration Analysis, and 

Enhances Efficiency of Rotating 

Machinery Maintenance 

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

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

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

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

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

1798-7024 (print), ISSN 1799-8670 (online). 

3/2017 maintworld 5

TECHNOLOGY 

Machine learning has typically been linked with industries such as transportation 

and banking, but there are many uses for machine learning within the industrial 

sector. This article focuses on four industries within the industrial sector 

that are primed to take advantage of the application of machine learning and 

leverage the many benefits it can bring. 

The Productivity 

Boost of the 

Century 

RICHARD IRWIN, 

Bentley Systems, 

richard.Irwin@ 

bentley.com 

x 6 maintworld 3/2017

TECHNOLOGY 

BEFORE STARTING, it is important to 

point out that there are many options 

and techniques available to gain more 

insight and make better decisions on the 

performance of your assets and operation. 

It all comes down to knowing what 

the best fit is for your needs and what 

type of data you are using. 

Machine learning makes complex 

processes and data easier to comprehend, 

and it is ideal for industries that 

are asset and data-rich. A great deal of 

data from various data sources are required 

in machine learning, and a data 

scientist or analyst may be needed to 

EARLY ADOPTERS OF 

MACHINE LEARNING ARE 

REAPING THE BENEFITS IN 

THE SPEED OF INFORMA- 

TION DELIVERY, COSTS, 

AND USEFULNESS. 

help set up and interpret the results. 

While it is possible to build your own ML 

platform, this design takes time, specific 

skills, and investment in a platform such 

as Microsoft Azure for a secure, private 

cloud platform for developers and data 

scientists. Alternatively, purchasing machine-learning 

capabilities off the shelf, 

as part of an asset performance management 

software solution, or outsourcing 

to a third party are options, provided you 

ensure input from in-house skills. 

Whatever path is chosen, the benefits 

machine learning can offer to big data 

are only just being brought to fruition. 

Opportunity is rapidly developing with 

productivity advancements at the heart 

of the data-rich industry in which you 

work. Here are some examples leading 

the way in this fast-moving digital transformation. 

Electric and Power 

We are all familiar with the term “smart 

grid” – the electrical supply network that 

utilizes digital technology and measures 

to detect and react to usage issues. In 

today’s turbulent times, electric utility 

companies are affected by ageing assets, 

increasing energy demand, and higher 

costs; the ability to recognize equipment 

failure and avoid unplanned downtime, 

repair costs, and potential environmental 

damage is critical to success across all 

areas of the business. 

Machine learning is augmenting the 

smart grid to better leverage and gain 

insight from the IoT with an enormous 

number of connected assets spread 

across a large network. Transformers, 

pylons, cables, turbines, storage units, 

and more — the potential for equipment 

failure is high and not without risk, so 

predicting failures with data and models 

is the new answer to keeping the network 

running smoothly. Another example 

of how machine learning helps the 

utilities industry is evidenced through 

demand forecasting, where predicting 

usage and consumption from numerous 

parameters can give a utility the advantage 

of being able to respond in advance, 

and balance supply with demand levels 

Smart meters can also be leveraged more 

individually so that customer recommendations 

regarding efficiency can 

be made. Machine learning also allows 

thermal images and video to be analyzed 

without the human eye to spot differences 

or anomalies in equipment. Additionally, 

asset health indexing can be 

leveraged to automate the analysis of extending 

asset life with machine learning, 

which is a low cost alternative to capital 

replacement. 

Oil and Gas 

In the oil and gas industry, the ability to 

recognize equipment failure and avoid 

unplanned downtime, repair costs, and 

potential environmental damage is 

critical to success across all areas of the 

business, from well reservoir identification 

and drilling strategy, to production 

and processing. In terms of maintaining 

reliable production, identifying equipment 

failures is one of the main areas 

where machine learning will play an important 

role. Predictive maintenance is 

the failure inspection strategy that uses 

data and models to predict when an asset 

or piece of equipment will fail so that 

maintenance can be planned well ahead 

of time to minimize disruption. With the 

combination of machine learning and 

maintenance applications leveraging 

IoT data to deliver more accurate estimates 

of equipment failure, the range 

of positive outcomes and reductions 

in downtime and the associated costs 

means that it is worth the investment. 

As well as predicative maintenance, 

the oil and gas industry has already started 

using machine learning capabilities 

in other areas. These include: reservoir 

modelling, where advanced analytics are 

used to make improved estimates on the 

properties of reservoirs based on historical 

data and models; video analysis 

that can be employed to detect patterns 

associated with anomaly detection; and 

case-based reasoning, which can help 

by siphoning out numerous parameters 

that account for well blow outs and leakages 

from a large example set of previous 

cases in order to come up with solutions. 

The application of machine learning has 

the potential to transform the oil and gas 

industry, which is even more crucial during 

the recent downturn in production 

and spending. 

Water Utilities 

Like the electric utilities mentioned previously, 

water companies also face the 

same challenges of an ageing infrastructure, 

rising costs, tighter regulations, 

and increasing demand. With that, they 

also share the same benefits that machine 

learning offers, such as identifying 

equipment failure before it happens — 

Common forms of 

machine learning 

techniques: 

SUPERVISED LEARNING – 

using “trained” data 

• Linear Regression - Linear 

regression is used when data 

has a range, such as sensor 

or device driven data, and is 

used to estimate or predict a 

response from one or more continuous 

values 

• Classification – Classification is 

typically used for data that can 

be categorized, such as whether 

an email can be classified as 

genuine or spam. 

UNSUPERVISED LEARNING – using 

data without labelled responses 

• Clustering – The task of grouping 

a set of objects then deriving 

meanings from hidden 

patterns in the input data by 

putting the objects into similar 

groups. 

• Neural Networks – A rule-based 

computer system modelled on 

the human brain’s processing 

elements. 


TECHNOLOGY 

not just to predict a failure, but also to 

identify what type of failure will occur. 

Other benefits of machine learning in 

the water industry include meeting supply 

and demand with predictive forecasting 

and making smart meters “smarter” 

to help curb waste, such as during water 

shortages. 

Water distribution is another area 

that can be optimized with the application 

of artificial intelligence. Machine 

learning can be used in this scenario to 

speed up the decision-making process 

of how demand can be met by analyzing 

how much water needs to be supplied 

from the various locations (reservoirs, 

desalination plants, and rivers), as well 

as the pumping considerations and 

water movement, including associated 

costs and constraints. Machine learning 

will help determine the optimal low-cost 

methods of configuring network transfers, 

optimizing supply options, enhancing 

the raw water supply network, and 

determining the cheapest time to transfer 

water across the network. 

Flood detection can utilize machine 

learning by analyzing data from sensors, 

weather, geospatial location, alarms, and 

more to provide precise predictions and 

classifications of when and where floods 

are likely to occur at any given time; 

these predictions are based on current 

and historical data from all sources. This 

information would help utilities save 

time and costs, reduce false alarms, and 

lessen the impact on the environment. 

Manufacturing 

Manufacturing has always been the main 

industry when mentioned alongside 

machine learning, and for good reason, as 

the benefits are very real. These benefits 

include reductions in operating costs, 

improved reliability, and increased productivity 

— three goals that relate to the 

holy trinity of manufacturing. To achieve 

this, manufacturing also requires a digital 

platform to capture, store, and analyze 

data generated by control systems 

and sensors on equipment connected via 

the IoT. Preventative maintenance is key 

in improving uptime and productivity, 

so greater predictive accuracy of equipment 

failure is essential with increased 

demand. Furthermore, by knowing what 

is about to fail ahead of time, spare parts 

and inventory can use the data to ensure 

they align with the prediction. Improving 

production processes through a 

robust condition monitoring system can 

give unprecedented insight into overall 

equipment effectiveness by monitoring 

air and oil pressures and temperatures 

regularly and consistently. Other areas 

MACHINE LEARNING IS IDEAL 

FOR INDUSTRIES THAT ARE 

ASSET AND DATA-RICH. 

of use include quality control optimization 

to ensure quality is consistent 

throughout the manufacturing process. 

For example, adaptive algorithms can 

be used to inspect and classify defects in 

products on the production line with pattern 

recognition to reject defects, from 

damaged fruit to deformed packaging. 

Digitalization and 

transformation with 

machine learning 

Early adopters of machine learning 

are already reaping the benefits in the 

speed of information delivery, costs, and 

usefulness. As the technology advances, 

each industry is learning from each 

other, further advancing the use and 

influence of artificial intelligence. This 

gives you more information and insight 

to make smarter decisions. Bentley 

Systems’ users are combining machine 

learning with Bentley’s other digitalization 

technologies to make this process 

even more beneficial – by making it 

model-centric and adding visualization 

dashboards, cloud-based IoT data, analytics, 

and reality modelling to machine 

learning, the result is a complete solution 

for operations, maintenance, and 

engineering. 

Having a machine learning strategy 

in place will give you unprecedented 

insight into your operation and will lead 

to serious benefits in efficiency, safety, 

optimization, and decision making. The 

digital transformation for industry is 

now at a tipping point, with technologies 

all converging at the same time – a 

whole range of problems that once took 

months to address are now being resolved 

in a matter of minutes, all thanks 

to machine learning. 


APM Solutions to 

Keep You on Target 

nexusglobal.com/apmsolutions

LEADERSHIP 

Developing 

Leadership 

in Maintenance 

and Reliability 

Does your organization have what it takes to be successful in leading maintenance 

and reliability improvement across the facility and the corporation? Below, we will 

share with you the story of one young leader who has had the opportunity to 

lead two different organizations. Let us see what we can learn from the successes 

and the failures that could advance your maintenance and reliability efforts. 


LEADERSHIP 

BILL LEAHY, RMIC, 

Senior Instructor 

Eruditio, LLC, 

BLeahy@ 

EruditioLLC.com 

SHON ISENHOUR, 

CMRP CAMA, Partner 

Eruditio, LLC, 

SIsenhour@ 

eruditiollc.com 

TWICE IN MY LIFE I have been charged 

with leading a group of 40 or so skilled 

workers. Once a success, and once was 

a glorious failure. In generalities, the 

situation was identical. A young leader, 

new to a profession, put in command 

of a highly skilled team with hundreds 

of years of collective experience. The 

expectation was that I had all the training, 

skills and natural ability to hit the 

ground running. As a Junior Army Officer, 

I had a good run as Platoon Leader. I 

affected change and together my platoon 

and I achieved our unit KPI’s. After a 

few more years of service I left the Army 

and took with me a high level of leadership 

confidence into the manufacturing 

world. I took on a cross-functional team 

of technicians and dreamt up a grand 

five-year Manufacturing and Reliability 

Improvement Plan for the site. Then I 

promptly crashed and burned. Despite 

past experience doing identical leadership 

tasks, I failed. To better understand 

the situation, I started asking what variables 

changed? This is what I discovered: 

I was spoiled in the Army. That system 

is established to ensure young leaders 

enjoy success despite themselves. 

Imagine a world where your boss, his 

boss, his boss, and his boss all held your 

job at some point in time. Even more, 

they had to be good at it to get promoted 

to positions of greater responsibility. 

Your senior employee is attached at your 

hip, trusts in and enforces your decisions, 

and never hesitates to provide 

necessary course corrections. There are 

many others just like you doing the exact 

same job. You are all friends and share 

best practices regularly. Everyone knows 

their job, has been trained to standard 

and is held accountable to it. Every procedure 

you need is published in easy to 

read manuals with pictures. And, there is 

a place called The Center for Army Lessons 

Learned that you can tap into. I realized 

it wasn’t so much me, but the expert 

support and training I received that 

was responsible for the success I had. 

Regretfully, this support system did 

not follow me into maintenance. No 

matter how many reliability engineering 

books I read, I could not prepare myself 

to stand alone in front of a maintenance 

team that had survived 12 maintenance 

bosses in as many years. The first year 

was exhausting and brutal. I acted alone 

while trying to reinvent the wheel. False 

starts and failed initiatives marked time. 

I quickly began questioning my competency 

and career path. 

What it boiled down to was, I needed 

the same sort of expert support I had 

in the Army. I needed: a coach to walk 

me through a Failure Mode Effects 

Analysis and Root Cause Analysis, a 

mentor to guide me through change 

management and work culture intricacies, 

peers to bounce ideas off regularly, 

and employees that were at a minimum 

aware of maintenance and reliability 

best practice, concepts, and language. 

I knew no one that had done the things 

I was reading about before. My boss 

hadn’t, I hadn’t, and my employees had 

never been exposed to it. So, I started at 

the top. I targeted the mill manager for 

sponsorship. As he became aware of reliability 

and maintenance improvement 

paybacks, resources became available. 

Technicians started attending training. 

I was provided opportunities outside of 

the company to develop my skill and become 

an expert in practical application. 

I also built my network of reliability and 

NO MATTER HOW MANY RELIABILITY 

ENGINEERING BOOKS I READ, I COULD NOT 

PREPARE MYSELF TO STAND ALONE IN 

FRONT OF A MAINTENANCE TEAM THAT 

HAD SURVIVED 12 MAINTENANCE BOSSES 

IN AS MANY YEARS. 


LEADERSHIP 

maintenance peers within the company 

and beyond. It started slowly but by end 

of year two the Manufacturing and Reliability 

Plan revision meetings evolved 

from a one man show to a cross functional 

team of leaders, techs, and operators. 

The maintenance plan transformed 

from an individual business venture into 

a cooperative. A maintenance culture 

that valued training, partnership, and accountability 

began to take root. 

Five Points to Success 

The Army made it simple and to the 

point. I didn’t understand the advantages 

their system provided and why 

it worked at the time. It took a year of 

constant failure and much reflection to 

realize how and more importantly why 

they placed so much value on training, 

partnering and mentoring. Here are the 

five things I took away from the Army 

and built into my reliability improvement 

strategy. 

First, find experience. Sources of 

1| expert knowledge are available, if 

not in your company they are certainly 

available outside of it though organizations 

like EFNMS (European Federation 

of National Maintenance Societies) and 

SMRP (Society of Maintenance and Reliability 

Professionals). You need these 

experts to help paint the picture of what 

a good facility can look like. If you have 

never seen it, it can be hard to picture in 

your mind. Ask to visit world class sites 

or collect real world examples and case 

studies from successful sites. You need 

this experience and these examples to 

explain it to your organization. 

Second, we encourage young 

2| maintenance leaders to network 

as much as possible. Look for others that 

are experiencing the same things you are 

that you can talk to and compare notes. 

You can find them at conferences, local 

chapter events, and possibly even in your 

own organization. 

Third, find a mentor who will answer 

your questions and push you 

3| 

along. This is someone who will show 

you what to do differently and work with 

you when you get stuck using a tool or 

process. 

Fourth, constantly advocate for additional 

training, for you and your 

4| 

staff, from sources beyond the company. 

This outside information brings new 

perspectives and ideas to keep the organization 

moving forward. 

Fifth, document and share. Create 

5| standardized processes and tools 

where you can and roll them out across 

the group of young leaders to facilitate 

both onboarding, benchmarking, and 

understanding. 

Now if you are trying to create a reliability 

culture at the corporate level, I 

would ask are you providing for these 

needs and ensuring these new young 

leaders have the support they need from 

all levels or are you leaving them to reinvent 

the wheel in a vacuum? 


Why gamble 

with Reliability 

education? 

eruditio is a 

sure bet. 

Your one stop source for the 

UT RMIC & SMRP certification. 

We're here to solve your problems. 

We know that education without application is just 

entertainment. That’s why we focus on building custom 

project based learning experiences to develop, refine 

and apply real-world skills. 

Our iBL ® Courses Include: 

• Planner Scheduler 

• Maintenance Manager 

• Reliability Engineer 

• Maintenance Engineer 

• Implementation Leader 

To do get your own Augmented Reliability playing 

card and see the iBL ® curriculum go to: 

www.iBLtraining.com 

+1 843-375-8222 

www.Eruditiollc.com/MW

CONDITION MONITORING 

Fig.1: Over 50% of machine failures are due to misalignment 

How to Identify 

the Root Cause of a 

Misalignment Condition 

GREG LEE, 

Senior Project 

Manager 

PRUFTECHNIK 

Inc. USA 

It is well known that misalignment is one of the greatest 

causes of failure in rotating equipment. In fact, research 

demonstrates that more than 50 percent of machine 

breakdowns are the direct result of poor alignment. But 

correcting misalignment can be a challenge. 

MISALIGNMENT CAN HAVE many causes. 

Alignment is not a static condition. 

Alignment changes when a machine 

warms up, its alignment can shift with 

the thermal expansion of its parts. When 

a machine vibrates, its skids can move 

and affect its alignment. When minor 

process parameters such as pressure or 

temperature are modified, alignment 

can change. Even the simple and natural 

succession of the seasons can alter alignment 

and put machine assets at risk. 

Effectively countering the factors that 

influence or alter alignment depends 

on understanding how the alignment 

14 maintworld 3/2017 

of your machine changes over time and 

with use. It depends on accurate and 

careful analysis of the trends in your 

alignment data. 

It Can Be Difficult to Identify 

the Cause of Misalignment 

The root cause of a misalignment condition 

is not always obvious. Vibration 

analysis might uncover a misalignment 

problem, but it won’t necessarily identify 

the reason for it. Capturing alignment 

data before equipment is removed or disassembled, 

even when maintenance is 

undertaken for non-alignment reasons, 

may, over time, reveal hidden causes of 

misalignment. Periodically checking and 

recording alignment conditions generates 

useful information about correctable 

conditions that, if addressed, will 

reduce breakdowns, increase productivity, 

and save money. 

Maybe it’s a 

Foundation Problem 

Capture and analysis of alignment data 

trends proved useful at a co-generation 

plant in the San Francisco area. In this 

real life example, a plant operator found 

that his machines needed realignment


every six months. The requirement was 

always the same, the turbine needed to 

be shimmed up another 0.05-0.1 mm. 

By analyzing the alignment trends over 

time, it was discovered that the turbine 

foundation, built on fill dirt in an area of 

land recovered from San Francisco Bay, 

was slowly sinking. Vibration analysis had 

identified the misalignment problem, but 

only analysis of the gap and offset alignment 

trends revealed the reason why. 

Maybe it’s a Weather Problem 

Capture and analysis of alignment trends 

also assisted in correcting pump alignment 

problems in a high desert environment. 

In this case, a pump and pipe assembly, 

which had been properly installed 

and aligned, was inexplicably running in 

and out of alignment. Again, vibration 

analysis exposed the misalignment, but 

it was analysis of alignment trends that 

identified the source of the problem. 

Trend charts revealed that seasonal temperature 

extremes were negatively affecting 

alignment. In the western American 

desert, summer heat often runs above 110° 

F (43° C), while in the winter tempera- 

Fig. 2: Measurement results showing 

machine misalignment 

Fig. 3: Trend diagram showing seasonal temperature 

changes and impact on the alignment condition 

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tures sometimes fall below 0° F (-18° C). 

The reason for misalignment was easy to 

identify when examining the trends in 

the coupling clearances. As the outside 

temperatures changed with the seasons, 

a corresponding misalignment of the 

pump and pipe assembly became readily 

apparent. 

Monitoring Machine Health 

Knowing the condition of your machines 

can help avoid or mitigate costly 

breakdowns and failures. While keeping 

an eye on vibration levels is one wellestablished 

way to monitor the health of 

rotating machines, capture and analysis 

of actual alignment data will take your 

understanding and preparedness to the 

next level. Instead of merely signalling 

that a problem is already occurring, 

periodically checking the state of your 

alignment may allow you to anticipate 

or even avert the need for major repairs. 

Fully understanding how alignment data 

changes over time is central to maintaining 

operational readiness and effective 

alignment protection. When alignment 

data are collected and presented graphically 

in the form of trend lines or charts 

with specially designed software, exploring 

and understanding alignment data 

trends is easy. Alignment trend analysis 

is especially useful in identifying problems 

due to: 

Thermal Expansion – As machines 

warm up or cool down, alignment can 

change significantly. A “hot” alignment 

can help, but it will not capture all the 

elements of the changing alignment 

condition. Over time, machines that are 

not setup and adjusted to accommodate 

thermal expansion and contraction reveal 

dynamic misalignment problems by 

their high rates of failure. 

Seasonal Effects – Seasonal changes in 

temperature can dramatically alter the 

alignment of rotating equipment. Where 

PERIODICALLY 

CHECKING AND RECORDING 

ALIGNMENT CONDITIONS 

GENERATES USEFUL 

INFORMATION ABOUT 

CORRECTABLE CONDITIONS. 

Fig. 4: Vertical offset and gap indicating a constant settling of the 

turbine over the past 1.5 years 

pumps or exposed piping are located in 

an outdoor environment, the equipment 

is exceedingly vulnerable to significant 

seasonal effects and temperature extremes 

that impact alignment. 

Alteration in Process Parameters – 

Even small changes in the temperature, 

pressure, or other operating parameters 

can alter the dynamic forces and alignment 

of machines. 

Sub-Structures or Bases – Machine 

bases, skids, or plinths can move causing 

changes in alignment. Relocation 

of machines to operating facilities that 

have different substructures or different 

degrees of rigidity or flatness can negatively 

affect alignment. 

Uncertainty in Vibration Data – 

Sometimes vibration data does not 

clearly uncover a misalignment condition 

in time. Periodic measurement and 

analysis of alignment data can help identify 

all of these problems before critical 

failures occur. At the end of the day, 

timely information about actual alignment 

conditions will always be the best 

weapon in your arsenal to counter the 

forces that trigger alignment problems. 

Capture and analysis of alignment data 

trends will provide that information. 



Enjoy 

Success 

with 

Small 

Vibrometers 

How can a simple vibrometer successfully 

detect defects? This article contains 

a guide for reading the measurement 

results and how to reliably determine 

machine condition. 

EXPERIMENTAL ARTICLES on vibration 

diagnostics are almost always written 

about using a powerful analyzer. From 

my long experience in this area, I have 

only exceptionally met with a description 

of using a simple vibrometer. The 

usual opinion among maintenance people 

is that a powerful analyzer is needed 

for a real diagnosis; this is a myth. In my 

opinion even with a simple vibrometer, 

90 percent of defects can be accurately 

determined, and for the remaining 10 

percent it will at least point you in the 

right direction. 

A Simple Vibrometer - 

What Is It? 

A simple vibrometer must be able to carry 

out at least two basic measurements: 

- RMS vibration velocity measurement 

in the 10-1000 Hz band (referred 


RADOMIR 

SGLUNDA, 

Managing Director, 

Adash Ltd., 

sglunda@adash.cz 

to as velocity in this article) 

- RMS vibration acceleration measurement 

in the 500-15000 Hz band (referred 

to as acceleration). 

If band frequency ranges are slightly 

different, this is not a defect. It is important 

that when measuring acceleration, 

speed frequency and its harmonics have 

been removed. The aim of measuring 

velocity is to detect mechanical defects 

such as an imbalance, misalignment, 

looseness and soft-foot. The purpose of 

acceleration is to determine the condition 

of the roller bearings and gears. 

If the vibrometer can also measure 

TRUE PEAK values, display time signal 

and evaluate the signal spectrum, then 

these measurement types will make the 

analysis even more reliable. 

The Basic Scheme 

of the Machine 

Simple machines have a drive part, usually 

an electric motor, and a driven part 

such as a fan, or pump. Both parts are 

usually connected by a shaft coupling 

and both shafts are mounted on rolling 

bearings. From now on we will refer to 

the driven part as “a fan”. 

A measurement point is a place on the 

machine where the vibration sensor is 

placed. 

There are standards that determine 

where the machine needs to be


measured, but we will not be dealing 

with them. They typically need many 

more measurement points than the 

five points that will be enough for our 

measurement. The machine has four 

roller bearings and we should select four 

measuring points as close as possible to 

these bearings. These four points must 

be radial, i.e. perpendicular to the shaft. 

Do not worry about whether to measure 

vertically or horizontally. You can 

choose any direction between these two 

directions. The last fifth point will be axial, 

i.e. parallel to the shaft. Put it on the 

coupling and it doesn’t matter whether 

it is on the engine or the fan. This fifth 

point is therefore perpendicular to the 

previous four. 

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

carried out without a pad, the results will 

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

We deliberately did not mention 

measuring with a sensor that has no 

magnetic base, and that is just pushed 

onto the machine by hand. This method 

is unrepeatable. Unfortunately, it is 

sometimes used in maintenance and 

so the results are disappointing. Sometimes 

the whole vibration diagnostics 

programme is rejected, the only reason 

being unprepared measuring points. 

THE USUAL OPINION 

AMONG MAINTENANCE 

PEOPLE IS THAT A 

POWERFUL ANALYZER 

IS NEEDED FOR A REAL 

DIAGNOSIS; THIS IS A 

MYTH. IN MY OPINION 

EVEN WITH A SIMPLE 

VIBROMETER, 90 PERCENT 

OF DEFECTS CAN BE 

ACCURATELY DETERMINED. 

Once the measuring points have been 

chosen, they need to be prepared for 

the measurement. It is not possible to 

simply take the sensor with a magnetic 

base and put it on the uneven surface of 

the machine. Measuring pads must be 

stuck on the selected locations before 

measurements are taken. They have a 

flat surface. In addition, they guarantee 

that you will always measure at the same 

machine location. The basic rule for taking 

measurements is to make sure the 

measurement conditions are 100 percent 

repeatable. That is exactly what the 

measuring pads guarantee. Let’s try, for 

example, 10 repeated measurements in 

one place i.e. put the sensor on the pad, 

measure it and then remove it from the 

pad. You will find that the measurements 

are almost identical. They will vary by 

Figure 1. Measurement pad glued onto the 

machine surface (1), magnetic base (2), 

acceleration sensor (3). 

How to Find Warning and 

Alert Vibration Levels 

The first measurement has already been 

taken and the results obtained. But what 

do the numbers mean? Are the vibration 

readings low or high? With what should 

the results be compared? The easiest 

way is to use ISO 10816, but the limits 

given here have one significant defect. 

They apply to machines with speeds of 

600-3000 RPM. Let’s suppose the fan is 

unbalanced. The centrifugal force that 

causes vibration will vary significantly 

for 600 RPM and 3000 RPM. The dependence 

of the force on the speed is 

quadratic, i.e. 2x higher speed means 4x 

higher force. Therefore, the weight of 

the heavy point on the rotor may not create 

a problem at 600 RPM but will cause 

the fan to destruct at 3000 RPM. The 

warning and danger limit values should 

depend on the speed. (See Figure 2) 

If several similar machines are 

measured, then the situation is simpler 

because we can compare the values from 

all machines. If we get results equal to 

1.8, 2.1, 1.9 and 4.5 from the same point, 

then it is obvious that 2.0 means good 

machine condition. A machine condition 

with a value of 4.5 should be investigated 

further. 

The first step deals with regular 

measurements and monitoring the 



Figure 2. 

vibration trend. If it is stable and has a 

permissible value, then the machine is in 

good condition and there is nothing else 

to do. If the value gradually increases and 

the warning threshold is exceeded, the 

second step of the evaluation must be 

carried out. 

The aim of the second step of the 

evaluation is to find the cause of the increased 

vibration. I will now describe the 

procedure of deeper analysis. 

Bearing Condition 

Needs Acceleration 

INCREASED ACCELERATION VALUE 

If the acceleration value has increased 

and the increase is only in one radial 

location, then it is easy. The problem is 

the poor condition of the roller bearing 

at this point. If the gears are measured, 

then the acceleration values can be increased 

in more places and it shows a 

problem with the gearing. 

Imbalance, misalignment and 

looseness need velocity 

IMBALANCE OR LOOSENESS 

The values are significantly increased on 

only one part (either the motor OR fan). 

If the increases in both radial directions 

are similar, then it is most probably an 

imbalance. If you have a signal spectrum 

at your disposal, you can find the significant 

value only on the speed frequency. 

If the increase differs significantly in 

both radial directions, or there is only a 

vertical increase, then it is most probably 

due to looseness. You should measure 

each machine foot. You would probably 

find significantly higher values on one of 

them. 

Electrical defect 

generates vibrations 

ELECTRICAL DEFECT OF THE MOTOR 

When the electric motor vibration looks 

like imbalance is the problem, then you 

should always also consider electrical 

defect. The electric motor may have 

winding defects, and despite this the 

vibration behaviour indicates an imbalance. 

Therefore a switch-off test on the 

motors should always be carried out. 

After switching off the power, one of two 

situations will occur. 

1) The velocity decreases slowly along 

with the rpm drop. This is a true imbalance. 

2) Immediately after switching off, 

the velocity increases for a very short 

time (1 sec), by a multiple and then drops 

to a very low value where it remains 

until the machine stops. This is an electromagnetic 

problem. The force field is 

not uniform and shifts the rotor off its 

mechanical centre of gravity. In the vibrations 

it will manifest as an imbalance. 

After switching off, the force is instantly 

lost and the rotor jumps back to the 

mechanical centre of gravity. This shock 

causes an increase in the value. Then the 

rotor starts spinning normally and the 

vibrations disappear. 

Mechanical imbalance 

Electrical fault 

MISALIGNMENT 

If the velocity in the axial direction increases 

(usually it is higher than in the 

radial), always check the coupling and 

the alignment. It is misalignment that 

causes vibrations in the axial direction. If 

you have a spectrum, you can find higher 

values on the speed frequency and several 

harmonics. 

RESONANCE 

The velocity significantly increases on 

both parts (motor and fan) and only 

in the vertical directions. Take measurements 

across the frame below the 

machine. If there is a low value where 

the frame is supported, and high values 

between the supports then there is a 

resonance problem. 

A coast down measurement or 

gradual reduction of speed (frequency 

changer) will help. In case of resonance, 

the vibrations will decrease dramatically 

with a small speed change. If the 

standard operating speed cannot be 

changed, the frame must be additionally 

reinforced. 

Those who do Nothing, 

Make no Mistakes 

A lot of maintenance staff are unnecessarily 

nervous about carrying out vibration 

diagnostics. Simple devices are developed 

just for those who have no deep 

knowledge. If you take regular measurements, 

you will find that you have a 

much better overview of the condition 

of your machines. You will also certainly 

notice a decrease in the number of unexpected 

temporary shutdowns. 

EVEN WITH A SIMPLE VIBROMETER, 90 PERCENT 

OF DEFECTS CAN BE ACCURATELY DETERMINED. 


The Uptimization Experts. 

What does 

DOWNTIME 

mean to you? 

marshallinstitute.com

INDUSTRIAL INTERNET 

IIoT Simplifies 

Predictive Maintenance Solution 

Deployment and Maintenance 

There is a revolution happening. It is a slow burn right 

now but it is slowly gaining momentum throughout the 

world. It is the Industrial Internet of Thing (IIoT) revolution 

and it will change the IT manufacturing environment 

dramatically over the next 20 years. 

STAN BRUBAKER, 

President of Beeond, 

Inc., 

stan.brubaker@ 

beeond.net 

IIOT IS NOT JUST A BUZZWORD, but a 

real phenomenon that is being driven 

by standards organizations like OPC 

Foundation, OMAC and AMT. Additionally, 

Germany has a strategic initiative, 

known as INDUSTRIE 4.0, that is leading 

the European Union into the IIoT 

world so their manufacturing sector can 

remain globally competitive. 

So, why is there such excitement 

around IIoT and what do the OPC 

Foundation (opcfoundation.org) Organization 

for Machine Automation and 

Control (omac.org) and The Association 

for Manufacturing Technology (AM- 

TOnline.org) organizations have to do 

with it? 

The OPC Foundation has developed 

the OPC Unified Architecture (UA) 

Specification which enables IIoT in 

the manufacturing environment. UA 

enables information to be easily passed 

between sensors, machines, controls, 

monitoring devices and the cloud in a 

highly secure, flexible and open way with 

no custom integration code. The OMAC 

and AMT organizations, in partnership 

with the OPC Foundation, developed the 

Packaging Machine Language (PackML) 

and MTConnect version of the UA specification, 

respectively. It is the combination 

of OPC UA with these established 

industry standards that enables a much 

simpler and lower cost predictive maintenance 

solution. 

Users Spend an Inordinate 

Amount of Time and Money 

on Solution Integration and 

Maintenance 

The Manufacturing community (users) 

is dissatisfied with the amount of time, 

expense and complexity required to 

integrate and extract key metrics and 

Figure 1 - OPC UA / PackML Enabled Production Line 

Stan Brubaker and Beeond, Inc. provide IIoT 

consulting services with a focus on OPC UA 

adoption as an enabling technology. Stan 

has 20+ years in the product development 

and manufacturing execution systems (MES) 

business. This time includes 8 years managing 

software product development followed 

by 15 years managing large MES programs 

and projects and helping manufacturing 

companies realize business value from technology. 

Stan has a Bachelor of Science in 

Computer Science and an MBA from Penn 

State University and is a certified Project 

Management Professional (PMP). 


INDUSTRIAL INTERNET 

statuses between and from the machines 

they purchase from their suppliers and 

deploy in their plants. Each supplier has 

a different approach, naming convention, 

communication protocols, metrics 

and statuses. The complexity grows exponentially 

as more and more machines 

are added to the plant. There is very little 

standardization. This is all changing. 

The combined efforts of the OPC Foundation, 

OMAC and AMT have created a 

standard specification that when applied 

makes all machines look and act the 

same. What they actually do, e.g. washing, 

filling, capping, labelling, and casing 

and palletizing, are very different, but 

they are easily integrated to each other, 

to supervisory systems, and to the Cloud 

with little effort. 

Key Predictive Maintenance 

Metrics are in the Standard 

Both the OPC UA PackML and the OPC 

UA MTConnect specifications include 

standard machine states and maintenance 

metrics, e.g. uptime, downtime, 

reason codes, etc. so that all machines 

look and act the same. And OPC UA allows 

this information to be automatically 

accessed in a secure and reliable way. 

In UA terms, each machine is a Server of 

information and the plant’s Predictive 

Maintenance (PM) solution is both a 

Client or consumer of information and a 

Server providing information to supervisory 

systems like a plant HMI or Cloud 

solution (see the high-level architecture 

depicted in Figure 1). 

The power of OPC UA is that solutions 

like PM, when brought online, can 

automatically Discover the machines 

that are available in the plant and the 

machines can automatically serve the 

PM solution the information they have 

and if the machines are UA PackML or 

UA MTConnect enabled the semantics, 

naming conventions, states, etc. are all 

the same. The OPC UA PackML specification 

reduces the integration effort 

and complexity of machine to machine 

and the machine to supervisory solution 

information exchange. 

In addition to a simpler, lower cost 

PM implementation in a single plant, 

the OPC UA PackML and MTConnect 

standards enable a standard global view 

of performance and PM across all plants. 

Implementing an enterprise, global solution 

historically entailed very significant 

implementation, software, and hardware 

costs. In this enterprise scenario, 

OPC UA with PackML and MTConnect 

Beeond's 5 - Step 

IIoT Adoption Process 

• Faster Time to Market 

• Lower Risk and Cost 

Figure 2. 

1 Assessment 

Create an assessment 

Scorecard that maps 

your application against 

the OPC UA specification 

2 Roadmap 

Define a development 

roadmap of phased 

releases with work 

effort estimates 

3 Training 

Train developers on how 

to implement OPC UA 

solutions 

4 Development 

Develop software to 

implement OPC UA 

working with your 

development staff 

5 Compliance 

Assure that your product 

is compliant and can be 

logo certified by the OPC 

Foundation 

provide the capture and aggregation of 

data the plant level and the Cloud easily 

consumes, and presents that data. Adding 

to the lower cost, some Cloud technologies 

now provide a PM solution out 

of the box. 

Best Practice for Adoption 

of OPC UA, MTConnect and 

PackML 

As manufacturers and technology vendors 

put their IIoT and automation 

strategies in place, OPC UA must be a 

major component of their strategy. Because 

OPC UA is so comprehensive and 

all encompassing, moving to IIoT enabled 

automation means your strategy 

must address infrastructure, security, 

co-existence, migration and information 

model. How to start the adoption 

process can be overwhelming, but there 

is a practical, common sense approach to 

adoption. 

Beeond, Inc. (www.beeond.net) has 

defined a 5-Step Adoption Process that 

will accelerate your move to IIoT while 

reducing risk, cost and time to value. The 

5-Step Process is structured and organized, 

so users and vendors realize value 

quickly and cost-effectively. 

The 5-Step IIoT Adoption Process 

(Figure 2) will help both manufactures 

and technology vendors adopt the OPC 

UA Specification from concept to certification. 

The 5-Steps are: 

1. Assessment: Assessment of the company’s 

IIoT business, product and 

automation goals and requirement; 

the result produces an assessment 

scorecard that will map current capabilities 

and goals against the OPC UA 

specification. 

2. Roadmap: Defining a plan that addresses 

migration strategies, a prioritized 

roadmap of functionality 

defined in a phased release strategy 

and recommendations on tooling and 

training. 

3. Training: Training of development 

staff on how to implement the OPC 

UA specification using appropriate 

commercial SDKs. 

4. Development: Development of the information 

model and software modules 

needed for compliance, and 

5. Compliance: Assurance that product 

releases are compliance with the 

specification and that OPC UA Logo 

Certification, if desired, will be successfully 

achieved. 

The Benefits of Adoption 

Users and vendors who adopt OPC UA 

PackML or OPC UA MTConnect will be 

able to deploy their Predictive Maintenance 

solution faster and realize: 

• Lower integration complexity and 

cost 

• Lower solution maintenance 

• Consistent enterprise level view 

of performance and PM across all 

plants. The 5-Step Adoption Process 

will accelerate their move to IIoT using 

OPC UA so they realize benefits 

such as: 

• Understand the value and the return 

on investment that OPC UA can bring 

to your organization 

• Do an assessment and develop an 

IIoT realistic adoption roadmap for 

your organization 

• Experts’ guidance and a practical, 

common sense approach will reduce 


LUBRICATION 

Bearing Grease Replenishment - 

On-Condition or 

Time-Based? 

ALLAN RIENSTRA, 

Director of Business 

Development for SDT 

International, allan@ 

sdthearmore.com 

Maintaining plant assets at an optimal state of lubrication is a topic receiving lots of 

attention. Maintenance and Reliability practitioners dedicate teams to the task, but 

not every organization achieves world-class results. 

AS MUCH AS 80 PERCENT of all bearing 

failures are attributed to poor lubrication 

practices including: 

• Using the wrong lubricant 

• Lubricant deterioration 

• Lack of lubricant 

• Too much lubricant 

• Contamination 

• Mixing grease types 

• Using sealed bearings, but still providing 

a grease nipple access point on 

the motor 

Figure 1 - Collecting ultrasound 

data with SDT270 while 

replenishing lubricant. 

One glaring mistake that contributes 

to early bearing failure is over/under 

lubrication. Over and under lubrication 

is the product of scheduling grease 

replenishment on a time-based instead 

of a condition-based schedule, and not 

knowing how much grease to inject. 

TOO OFTEN BEARINGS ARE 

BEING FED NEW GREASE 

BEFORE IT IS REQUIRED. 

OTHER TIMES THE GREASE 

GUN COMES OUT TOO LATE. 

Too Often - Too Late 

Too often bearings are being fed new 

grease before it is required. Other times 

the grease gun comes out too late. 

Some lubrication technicians guess 

at the quantity of grease to inject and 

do not even know how much grease is 

dispensed with a stroke of their grease 

gun. Bearing manufacturers provide 

formulae for calculating a theoretical 

grease capacity for each bearing, but not 

everyone knows how to use them. Still 

others simply follow guidelines given by 

the motor manufacturer. Often this “bad 

advice” is stamped directly on the motor. 

To drive home this point, Haris Trobradovic, 

one of SDT’s corporate trainers 

recently delivered training to a petrochemical 

facility in the Middle East. 

- During the training, we performed 

measurement practice on several machines 

(Figure 1). One of the machines 

was a fan, scheduled for re-lubrication a 

few days later, recalls Haris. 

- The customer’s standard greasing 

practice is to follow the manufacturer’s 

recommendations for both interval and 

amount. In other words, they grease on a 

time-based schedule and trust the motor 

manufacturer to guide on quantity. 

Trobradovic used the opportunity 

and performed re-greasing exactly as 

recommended by the OEM, even though 

the Condition Monitoring team had a 

different opinion. Their ultrasound data 

did not indicate any need for grease replenishment. 

The CM team members are 

strong advocates for on-condition lubrication 

and doing away with time-based. 

Following the facility’s lubrication 

Figure 2 - Two fan bearings with 

different load. Why do they share the 

same grease replenishment protocol? 


LUBRICATION 

procedure raised several red flags. Figure 

2 shows two bearings driving the 

fan. Why would two identical bearings, 

but with different loads, have the exact 

same grease replenishment protocols? 

Maybe it is purely out of convenience; 

since the lubricator is there to grease the 

drive end bearing, a few strokes might 

just as well be pumped into the nondrive 

end at the same time. 

Another issue that disturbed the SDT 

Figure 3 - OEM instructs the owner to 

grease on a time-based schedule without 

considering the operating environment. 

trainer was the instructions stamped on 

the motor plate (Figure 3). This stamp 

instructs the owner of the motor to add 

32.7 grams of grease (grease type not 

identified) every 3,068 operating hours. 

Haris wondered if the OEM took into 

consideration the installation of the 

motor in a climate that is very hot and 

humid in the summer time, but cold, 

snowy, and dry in the winter. 

Don’t Mix Incompatible 

Grease Types 

One refreshing fact was an additional 

plate (Figure 4) with details about the 

grease type used in the bearing. Mixing 

incompatible grease types is an oftencited 

cause of premature bearing failure. 

This same reminder is provided by the 

SDT LUBExpert Ultrasound Tool. Prior 

to beginning a lubrication task LUBExpert 

reminds the operator of the correct 

grease type to use. 

Figure 4 - This motor had a secondary 

plate reminding lube-techs which grease 

type to use. 

Continuing with the experiment, 

Haris and the CM team attached the 

grease gun to the SDT equipment and 

greased the drive end bearing following 

OEM recommendations. Figure 5 is a 

screen shot captured from UAS, the companion 

software to LUBExpert. The top 

trend is the drive end bearing. Within 

four minutes the overall RMS increased 

by 7 dBµV while the Crest Factor and 

Peak spiked sharply. 

Figure 5 - Top trend graph illustrates drive 

end bearing after lubrication. It is badly 

over greased. 

The bottom trend is from the nondrive 

end bearing. Adding the requisite 

amount of grease had no positive outcome 

for the Overall RMS, which stayed 

stable at 26 dBµV. The drop in Crest Factor 

and Peak readings however, indicates 

the bearing may be entering a failed 

state. More frequent condition monitoring 

with complimentary technologies 

such as vibration analysis will ensure 

any machine downtime is scheduled on 

the client’s terms, not the machine’s. 

For those unfamiliar with these data 

formats, Overall RMS, Max RMS, Peak, 

and Crest Factor are unique condition 

indicators developed by SDT to bring 

analytical meaning to ultrasound STATIC 

data. Sadly, following OEM procedure 

resulted in over lubrication of the DE 

bearing. 

To drive home the point, Haris also 

captured DYNAMIC time signals from 

the drive end bearing. As seen in Figure 

6 the time signal before (bottom) and after 

(top) reveals new peaks and impacts 

Figure 6 - Dynamic data from drive end 

shows the emergence of defects (top) after 

bearing was over-greased following OEM 

recommendations 

forming. Over lubrication causes pressure 

to build inside the bearing. Ideally, 

the oil wants to feed from the thickener 

to form a thin film between the rolling 

elements and the race. It can’t do this if 

there is too much grease and pressure. 

The result is increased friction and impacting, 

two phenomena easily detected 

with ultrasound specialty tools like 

SDT’s LUBExpert. 

The Important Role of 

Lube-Techs 

Finally, Haris collected DYNAMIC time 

signals on the non-drive end bearing. In 

Figure 7 the bottom time signal shows 

dominant peaks that are clearly nonsinusoidal 

and indicative of impacting. 

After lubrication those peaks are gone. 

It appears that replenishing the grease 

in the non-drive end had some positive 

benefits, and those benefits are clearly 

illustrated in UAS time view. 

Figure 7 - The non-drive end bearing has 

defects as shown by this dynamic time 

signal. Ultrasound assisted lubrication with 

SDT allowed the CM team to identify this 

potential fault. 

The bottom line is that following OEM 

recommendations to replenish lubrication 

on a time-based or time-in-service 

protocol are proven wrong time and 

again. Following the greasing instructions 

stamped on the motor plate led to 

the drive end bearing being over greased 

and reducing life expectancy. 

Another interesting takeaway here 

is that, while an ultrasound lubrication 

solution – LUBExpert – was used 

to monitor the effects of adding grease, 

the added benefit for the CM team is 

the indication that a failure state may 

exist. There was a day not too long ago 

when the lubricator was counted on for 

keeping his finger on the pulse of the 

plant. Solutions like SDT’s LUBExpert 

are restoring important responsibilities 

to a task that recently has been given to 

“lower-skilled” tradespeople. 

It is past time that lube-techs be recognized 

for the important role they can 

play contributing to plant reliability. 


Road Map to Operational 

Readiness and Asset 

Performance 

Ensure a Safe, Reliable, and Compliant Operation 

Achieve business goals with a risk-based approach to asset management. Bentley will help get you there. 

Leveraging 30 years in design and visualization innovations, Bentley is at the forefront of engineering software. 

Combining 2D/3D plant models and point clouds with engineering information and asset performance management, 

Bentley delivers an enterprise platform to manage assets throughout their entire lifecycle. 

The visual workflow supports both greenfield and brownfield operations; bridging the gap between CAPEX and OPEX 

and enabling a sustainable business strategy for operational excellence and safety. 

© 2017 Bentley Systems, Incorporated. Bentley, the “B” Bentley logo, and AssetWise are either registered or unregistered trademarks or service marks of Bentley 

Systems, Incorporated or one of its direct or indirect wholly owned subsidiaries. Other brands and product names are trademarks of their respective owners.

Assess risk based on failure severity, likelihood scores, 

and confidence assessment. 

Visually navigate assets. 

Enterprise Platform for Asset Integrity, Reliability, 

and Performance: 

• Reality and geospatial context 

• Asset lifecycle information management 

• Asset strategy development 

• Risk-based inspections 

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• Risk assessment and work prioritization 

• ISO 55000 implementation 

• Change and configuration management 

• Operational analytics 

• Enterprise interoperability 

Learn more at www.bentley.com/AssetWise 

Or call 1-800-BENTLEY

ASSET MANAGEMENT 

Using Technology and Innovation to 

Manage 

Mega-Maintenance 

Challenges 

Imagine this as your workweek 

routine: travel to 

work, grab your tools and 

climb 80 to 100 metres 

to your job site, on occasion 

your climb is 160 

meters. Sometimes, before 

you can make your climb, 

you take a boat or a helicopter 

to the job. That’s 

the life of someone who 

maintains onshore and 

offshore wind turbines. 

Although the renewable 

energy industry presents 

some extreme issues for 

maintenance and service, 

every industry has uptime 

challenges. 

JENS OGRABEK, 

Services Manager for 

Wind at Moog, 

jograbek@moog.com. 

MAINTENANCE MANAGERS in any industry 

are looking for service and repairs 

at increasingly faster turnaround times 

as well as less costly parts. Equipment 

makers have to figure out how to provide 

not only routine service at a faster 

pace but also handle priority requests 

without slowing down the rest of their 

service business, at the risk of delaying 

other customers’ repairs. The stakes are 

high on all sides. To illustrate that, let’s 

explore an example from the wind industry 

where we applied technology and 

service options for more reliability and 

cost savings. 

When a windfarm operator keeps a 

turbine free from unplanned maintenance 

and running at peak efficiency, 

it directly contributes to a company’s 

revenue and profit. If something does 

go wrong, every minute matters. When 

a wind turbine comes offline due to unplanned 

maintenance, the average daily 

cost to a windfarm is several thousand 

Euros. 

According to a 2011 ReliaWind research 

report, pitch system failures 

account for 23 percent of all downtime 

in wind turbines. This is more than 

any other component or system of the 

turbine. The ReliaWind report 1 goes on 

to note that pitch systems tallied the 

highest percentage of all component 

failures in wind turbines at more than 21 

percent. 

When compared to the size of a wind 

turbine, a pitch control system appears 

1 “Reliability-focused research on optimizing wind energy system design, operation 

and maintenance: Tools, proof of concepts, guidelines & methodologies for a new 

generation.” 



Inside a wind turbine 

hub, the pitch system 

adjusts the angle of 

the rotor blades. 

REDUCING THE FREQUENCY AND COST OF MAINTENANCE 

TAKES A COMBINATION OF NEW TECHNOLOGY AND CREATIVE 

OPTIONS FOR SERVICE 

A worker exits a wind turbine’s nacelle 

and moves onto the hub to which each 

blade attaches. 

small. But pitch systems keep a turbine 

running and ensure the safety of the 

turbine in the event of high winds or 

catastrophic events. The pitch control 

system monitors and adjusts the inclination 

angle of the rotor blades and thus 

controls the speed of the rotor. Although 

these systems play an outsized role, they 

account for less than three percent of a 

wind farm’s capital expenses. 

I work for a global company, Moog 

Inc., which makes high-performance 

motion-control technology, including 

pitch systems. While we are always 

analyzing market trends and developing 

new solutions, our services business is 

where we reconnect with our customers, 

maintenance issues and technology, 

both legacy products and new ones. 

With the ReliaWind report above as 

context, we saw that windfarm operators 

were struggling with a variety of manufacturers’ 

pitch systems. With a goal of 

alleviating maintenance and service issues 

as our focus, we set out to develop 

a new pitch system that required 50 

percent less maintenance than products 

already on the market made by other 

manufacturers and wind turbine designers. 

Our new system had fewer working 

parts, and we improved the design 

through using ultra-capacitors, instead 

of batteries, to eliminate backup power 

failures and periodic maintenance. 

By selecting AC synchronous motor 

technology (i.e., brushless, no fans for 

cooling), our engineers improved pitch 

system motor reliability and reduced periodic 

maintenance compared with the 

AC Induction or DC motors currently 

used by the wind turbine OEMs. These 

improvements are helping these new 

pitch systems increase reliability over 

existing industry designs by a remarkable 

223 percent. 

Case in Point 

Reducing the frequency and cost of 

maintenance takes a combination of 

new technology and creative options for 

service. For example, one of our pitch 

system clients had a maintenance issue 

in Brazil, a country with government 

regulations that can sometimes create 

challenges when procuring parts. Some 

of the wind turbines in Brazil are located 

in extremely remote locations. So our 

plan was to do everything possible to 

eliminate unscheduled service and be 

prepared should something happen. The 

customer simply couldn’t afford to wait 

weeks for new parts and repairs made 

from Europe. All parties concerned realized 

it was cheaper to scrap the parts in 

Brazil and send a part via a local, authorized 

supplier. Our local office in Brazil 

worked out a service agreement with our 

customer in Brazil to send rotable parts 

as clients needed them, and we, in turn, 

would keep an inventory of 15 to 30 core 

components of the pitch system for the 

customer to have on hand. As part of the 

plan, if we learned there was a critical 

volume of parts and repairs needed, we 

would organize a cost-efficient way of 

repairing the broken parts. We would do 

this by calling on either a localized partner 

or Moog technicians with technical 

assistance from our global support team. 

As our customer’s inventory of reworked, 

rotable parts is depleted and 

new requests come in for repairs, we 

have a trigger point at which we repair 

any damaged parts and return them to 

our original factory standard. The repairs 

are not immediately made to the 

client’s damaged parts; instead the client 

receives a refurbished, like-new item 

that our local supplier may have received 

weeks before from another customer. By 

eliminating the need to handle separate 

components inside each pitch system, 



we have expedited service and enabled 

our clients to get their wind turbines 

back online much faster. This improves 

the Levelized Cost of Energy, or the net 

cost to install and operate a wind turbine 

against expected energy output over the 

course of the turbine’s lifetime (incentives 

excluded). And with rotable stock, 

we have enabled our customers in places 

like Brazil and elsewhere to reduce inventory. 

Tips and Strategies for Service 

Whether you are a maintenance manager 

relying on service or a manufacturer 

providing service, improving repairs 

takes flexibility. There has to be a willingness 

on all sides to think along new 

lines if you want to improve the way you 

deliver service and make repairs. Due 

to the challenges of wind turbine maintenance 

on a global basis, we analyzed 

ways we could better meet our wind energy 

customers’ expectations. 

We sat down with our customers to 

look for innovative ways to help them. 

In our case, we looked at the problem in 

two ways: First, how could we and our 

supplier solve the service problem in a 

way that best helped our customer? And, 

second, in what ways could we do this to 

ensure greater reliability of our systems 

and save maintenance costs. 

To help those customers not ready 

for an entirely new system, we are also 

providing retrofits using the ultra-capacitors 

and have seen vast improvements 

in less downtime due to backup failure as 

well as maintaining old battery systems. 

As a company we take what we learn on 

new systems and try to provide the same 

benefits for our retrofit customers. 

An additional service offering that 

Moog has made available to its clients is 

hands-on training on the exact system 

in the turbine, and on a scale that would 

truly make them capable of solving many 

of their own challenges and problems in 

the field without the need for Moog service 

personnel on-site. Our 800-squaremetre 

facility in Unna, Germany, provides 

technical training programmes to 

Moog’s global wind energy customers. 

At the centre, a team of expert trainers 

delivers customer training programmes 

ranging from a basic introduction to 

more advanced and focused engineering 

courses on products and systems. It is an 

investment that pays off for both our clients 

and Moog. A trained technician can 

diagnose, repair and restore a wind turbine 

to full operation in a fraction of the 

time it might take to remotely support 

an untrained technician. Overall, that 

will reduce the turbine’s downtime. 

In conjunction with the training centre, 

we also offer an around-the-clock 

help line. But even the help line is more 

efficient when the client placing the call 

has received a level of training that helps 

our services staff pinpoint a problem 

much faster. 

The training approach to service and 

maintenance has been so successful that 

we introduced a similar concept for our 

clients in China. We have been able to 

help our clients reduce downtime and 

the cost of energy by: 

• introducing new technologies like 

our latest pitch system, which is 

easier to maintain and can be monitored 

remotely; 

• adopting the concept of rotable stock 

and on-site support; and 

• providing quality training. 

And, ultimately, that spells an approach 

to maintenance and service that adds up 

to a reduced cost of producing energy for 

our customers. 

WHEN A WINDFARM OPERATOR KEEPS A TURBINE FREE 

FROM UNPLANNED MAINTENANCE AND RUNNING AT PEAK 

EFFICIENCY, IT DIRECTLY CONTRIBUTES TO A COMPANY’S 

REVENUE AND PROFIT. 


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MAINTENANCE MANAGEMENT 

Elements of a 

Good Preventive 

Maintenance 

Program 

Pages from 

CMS book. 

If your preventive maintenance program does not have the right content, it 

will never generate the desired and possible results. If you haven’t updated the 

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

also the wrong activities. A good PM program has 90% of all PM activities done 

as inspections while equipment is running. 

CHRISTER 

IDHAMMAR, 

Founder and CEO of 

IDCON INC., Raleigh 

NC, USA, 

info@idcon.com. 

CLASSICAL EXAMPLES of wrong and excessive 

PM are those activities on V-Belt 

drives, couplings and many other components 

with safety guards. Many PM 

programs suggest weekly inspections 

of these components by maintenance 

and at every shift by operators. On top 

of that, a shutdown PM is also done. The 

fact is that the design of most guards 

makes on-the-run inspection of the 

components impossible, and it doesn’t 

make sense to inspect something that 

cannot be seen. 

Many guards are big and heavy, so it 

can take two crafts people several hours 

to remove the guards, do the inspections 

and replace the guards during a shutdown. 

Even worse, if they find a problem 

on the component during the inspection 

and it has to be corrected before start up, 

this could lead to a prolonged shutdown 

and production losses. 

A correctly designed guard allows for 

inspections on the run (see Figure 1). In 

a route based inspection program, each 

of these inspections takes an average of 

three minutes including walking time. If 

a problem is found during these inspections, 

a planned and scheduled corrective 

maintenance action will be done 

when the opportunity presents itself. 

To decide the right content, you must 

understand three things: 

a. The consequence of component 

breakdown 

b. How failure can be detected 

c. How long before component breakdown 

can failure be detected 

Consequence of a Breakdown 

A breakdown is defined as the point in 

time when a component’s function ceases. 

The consequence of a breakdown can 

be prioritized in the following groups: 

a. Personal or environmental damage 

b. High costs for production lasses or 

Figure 1: The guard on the left is an 

example of a bad design for on-the run 

inspections. The one on the right is a good 

guard design for on-the-run inspections. 


MAINTENANCE MANAGEMENT 

maintenance to correct breakdown 

c. Preserve value 

As a first step, we advise not to go 

into any elaborate and time-consuming 

evaluation to find the criticality of equipment; 

this can be done later. We use 

the following fast approach to evaluate 

criticality: 

a. What will happen if this equipment 

breaks down? For 90% of equipment 

the answer is given by reading the 

nameplate of equipment and understanding 

the process. If there is spare 

equipment, you can find out how fast 

the spare equipment can be started 

b. Ask operators. If you do not know 

the answer to the first question, you 

should ask an operator. That should 

take care of another 50% of the remaining 

questions. 

c. Consult process and instrumentation 

drawings. It is bad if the operator does 

not know the answer, but it also identifies 

a need for training. Together, 

we will look at a process and instrumentation 

drawing to learn what 

will happen if the equipment breaks 

down. This will answer most of the 

unanswered questions. 

Using this screening process you only 

need to analyze what is important to analyze 

and you can save more than 90% of 

time as compared to processes suggested 

in Reliability Centered Maintenance and 

similar programs. 

Using the above approach, the next 

step will be to set up the right PM for 

each component (Coupling, valve, cooler, 

etc.) of the equipment (e.g. Hydraulic 

system). 

Documentation and Training 

After you have selected the right PM 

procedure, you need to document the 

procedure. It is important to decide on 

the document format, because it should 

be used to train people and improve the 

procedure in the future. Remember, in 

this case we are talking about basic inspection 

methods, not predictive maintenance 

(PdM) methods such as vibration 

analysis and wear particle analysis. 

It is easier AND safer to describe a 

method with pictures than words. The 

document also stands a better chance to 

be read and understood when it includes 

pictures. IDCON’s Condition Monitoring 

Standards books have 100 of the 

most common components documented 

in this type of format. 

At bare minimum, you need to include 

“what”, “how”, and especially 

Is it 

Practical? 

• 

NO 

Go to the next 

group 

YES 

NO 

“WHY” an inspection should be done. 

It does take time to create these documents, 

but once you do, the document 

can be re-used for most all components 

of the same type, for example a coupling. 

Frequencies and other values unique 

to the individual component will be 

described in the route list or in a hand 

held device. Do not make the mistake of 

assuming that crafts people or operators 

know how to inspect components. 

In our experience, crafts people have 

been trained to do repairs and trouble 

shoot existing problems. Very few have 

been trained in inspections to discover 

problems before they are actually problems. 

Much of this training is a thought 

process; you need to teach people to 

think about inspections and anticipate 

latent problems. 

At a minimum, training needs to 

include inspection methods for most 

common components and systems and 

a basic knowledge of instruments and 

tools such as high intensity lists, strobes, 

hand held IR instruments, optical tools 

and leak detectors. 

• 

Do they know YES 

• how? 

• 

• 

NO 

Can they be 

trained in > x 

minutes 

YES 

Implement 

task 

• 

Decision cycle 

Assign Resources 

It seldom works well to say, “PM is priority 

1 and we will assign different people 

to do it as we see the need.” Or worse 

still, “Our team decides who will do inspections 

today.” Trying to do it this way 

almost guarantees the PM effort will fail. 

Another common mistake is to assign 

the night shift to do PM when they have 

nothing else to do. The reason for having 

shift maintenance people is so they 

can respond to possible emergencies. 

If there are no emergencies, they are 

not needed on the shift and they can be 

moved to daytime work. The best results 

are always achieved when special people 

are assigned to do inspections on a full 

time basis. 

Assigning dedicated inspection resources 

garners the following: 

a. The right people to do the inspections, 

including in or adjustments and 

repairs 

b. The right people trained for this 

unique work 

c. The ownership and interest for PM 

that is necessary for continuously updating 

and improving PM work. 

d. An easier situation to manage. It can 

be very tempting to pull the people 

who are supposed to do PM to do 

emergency work. 

Wherever the assigned resources (PM 

inspectors) report to in your organizational 

structure, we advise they work 

very closely with the supervisor in the 

area where the inspections occur. They 

must report any findings and what they 

have inspected to the supervisor/area 

leader once or twice a day. When they 

have completed the route, they should 

do some of the repairs and adjustments 

that are the results of the inspections. 

This cuts back on administration and 

eases up the friction that can develop 

between PM inspectors and the crafts 

people who have to do all of the repairs. 

It is also important that PM inspectors 

start all routes with an interview 

with the operators in the area; this not 

only improves communication but also 

the on-the-job training of operators. The 

ultimate goal should be to have the operators 

do the majority of PM inspections. 

After you have decided the PM activity 

that needs to be done and the frequency 

you decide who should do it. The 

choices (in order of preference) are: 

a. Operator 

b. Area Maintenance- Mechanical, Electrical, 

Instrumentation crafts person 

c. In house expert, for example Vibration 

Analysis or Wear Particle Analysis 

d. Outside expert, for example X-ray, 

Acoustic Emission 


BENCHMARKING 

Demonstrating 

Value with 

Benchmarking 

How can the service offering that creates the highest value to the customer be 

identified? How can industry-wide experience-based data and knowledge be exploited 

to provide, and continuously improve asset management services. 

SUSANNA KUNTTU, 

VTT Technical Research 

Centre of Finland Ltd., 

susanna.kunttu@vtt.fi 

HELENA 

KORTELAINEN, 

VTT Technical Research 

Centre of Finland Ltd., 

helena.kortelainen@vtt.fi 

SUSANNA HORN, 

Outotec Oyj, susanna. 

horn@outotec.com 

MANUFACTURING, mining and process 

industry companies around the world 

are looking for comprehensive solutions 

to raise and keep the overall equipment 

efficiency (OEE) at a high level. From 

the service provider’s point of view, this 

demand requires a deep understanding 

of the customer´s operation and 

maintenance processes, and of the 

various aspects affecting the business. 

In a global operation, service sites are 

seldom comparable: the installed base 

(fleet), environmental conditions, maintenance 

practices and processed raw 

materials can vary significantly - among 

other issues. Service companies focus 

on providing the customers with highest 

value services to improve their asset performance. 

Customer value is, however, 


case-specific. The solutions provided to 

one customer might not be as valuable to 

the next one due to e.g. customer-specific 

competences or external constraints. 

Benchmarking is a widely-used 

method that allows a company to compare 

its own practices and processes to 

the practices applied in the best firms of 

the industrial branch. A typical objective 

is to find justified development targets 

- and to benefit from existing good practices 

in the industry. Service providers 

could exploit benchmarking approaches 

together with their customers when 

looking for development needs in the 

asset management practices. Among 

many problems concerning benchmarking, 

one challenge is to make companies, 

plants or production lines and service 

site comparable. 

Benchmarking is not 

a single method 

The commonly applied benchmarking 

procedure has been the comparison of 

the average values of the particular industrial 

sector with the company’s own 

values (Komonen et. al 2011). In practice, 

benchmarking approaches make 

use of a variety of qualitative or quantitative 

methods. Qualitative methods are 

able to provide detailed and insightful 

benchmarking information if the number 

of involved companies is modest. 

Quantitative methods, in turn, provide a 

more efficient way to collect and analyze 

large data sets producing benchmarking 

information from a large number of 

companies. The benchmarking method 

and tool presented in this article is quantitative 

and requires data from several 

companies. 

Service provider can utilize 

benchmarking to develop 

customer service 

The benchmarking tool helps to identify 

and visualize potential sources of value. 

The benchmarking method promotes 

service providers’ ability to recognize 

improvement potential in customer’s 

asset management practices and the 

ability to find improvement actions for 

the current situation. The method for 

demonstrating value with benchmarking 

(Valkokari et. al. 2016) was developed in 

co-operation with Outotec that provides 

asset management services to the mining 

industry. The developed benchmarking 

approach is generic and applicable to 

other industries. 

The quality and plausibility of the 

data analysis results depends always on 

the quality of used data. Benchmarking 

methods make no exception. Benchmarking 

is typically used by an organisation 

that wants to compare own level of 

productivity or OEE, or some other key 

performance indicators with other companies 

in the same industry. If a service 

provider carries out the benchmarking, 

the potential customer may question 

the result due to possible commercial 

interests. Thus, transparency of the data 

collection and the data analysis is crucial 

for the credibility of the results. To make 

benchmarking transparent, the service 

provider and the customer should carry 

out the data collection and analysis in 

close cooperation. The common effort 

also provides a well-structured opportunity 

to discuss aspects related to e.g. 

the maintenance function and its successfulness.

BENCHMARKING 

Benchmark among similar 

companies 

In quantitative benchmarking methods, 

the basic assumption is that the companies 

are similar enough to be compared 

if they operate in the same industrial 

branch. In real life, the diversity of the 

companies can be extensive and poses 

a major drawback. If the benchmarked 

companies differ too much from each 

other, the benchmarked company is 

barely able to find the right development 

targets or even recognize the companies 

that should be a valid reference group. 

With the proposed approach based on 

categorizing sites to comparable units 

and benchmarking them against each 

other, the best practices will depend on 

the business environment. Thus, the 

first step is to recognize similar kinds of 

companies that can best learn from each 

other. 

In this context, similarity means that 

companies are comparable according to 

the aspects affecting asset performance 

and asset management practices. As the 

focus is in the development of maintenance 

service offerings, the benchmarking 

method categorizes sites or 

plants according to their maintenance 

environment. ”A maintenance environment” 

collects together the data arising 

from those sites that are similar enough 

with respect to external aspects affecting 

maintenance activities as illustrated 

in Figure 3. Maintenance environments 

describe features that affect the requirements 

for the maintenance function and 

include maintenance policy and maintenance 

activities. Features describing 

a maintenance environment include for 

example: availability of competent employees, 

climate effect on maintenance 

conduction, life cycle phase of equipment, 

maintainability of equipment, etc. 

From a service provider’s point of view 

these aspects are external and cannot be 

controlled by a service provider. 

Data collection 

The benchmarking method requires a 

quantitative data set that contains variables 

about the maintenance environment, 

applied maintenance practices 

and level of success. CMMS or other 

databases seldom contain such statistical 

data that is relevant from the benchmarking 

point of view. Thus, part of the 

method development was to establish a 

questionnaire for the data collection. 

Figure 1. 

Example of 

data collection 

questionnaire 

The questionnaire includes 34 questions 

that help to categorize the sites to 

different maintenance environments, 

recognize maintenance practices and 

calculate a key performance indicator 

to assess the successfulness of a site (see 

Figure 1). The service provider carries 

out the data collection in normal business 

negotiation situations. For this 

reason, the length of the questionnaire 

has to be reasonable and the questions 

should be easy to answer. The number of 

the questions is as small as possible and 

whenever possible the questionnaire 

offers ready alternatives. Pre-defined 

answer alternatives also support automated 

data analysis that allow discussion 

about results immediately after entering 

the data items. 

Figure 2. 

Main phases of 

benchmarking 

Figure 3. User 

interfaces of the 

benchmarking 

tool, which 

allow data 

collection and 

data analysis in 

a meeting with 

a customer 

Developing targets 

based on benchmarking 

Benchmarking is a tool to find out potentially 

weak points in the operation 

and offers an input for detailed discussion, 

and planning and prioritizing for 

development actions. In the developed 

benchmarking method a site under study 

is, based on the questionnaire entries, 

categorized to one of the pre-defined 

maintenance environments according to 

its similarity index value. The similarity 

index indicates the closeness of the site’s 

answers to the profile of a pre-defined 

environment. As illustrated in Figure 2, 

all sites belonging to the same maintenance 

environment are extracted from 

the benchmarking database for further 

analysis. The best sites of a particular 


BENCHMARKING 

maintenance environment are defined 

according to the values of key performance 

indicators, like availability or 

maintenance cost divided by equipment 

replacement value. Comparing maintenance 

practices between the benchmarked 

site and the best sites points out 

the differences in maintenance practices 

applied in operation and management. 

Investigating reasons and effects of 

these differences can reveal targets 

for development actions of the benchmarked 

site. 

Benefits from a service 

provider point of view 

Outotec Service Business Development 

is a function that has been actively looking 

for new ways of providing value 

to the customer. By developing the 

benchmarking concept in cooperation 

with different departments within the 

company as well as with certain customer 

sites, the development team has 

been able to structure the data gathering 

process. Moreover, it is able to better 

utilize installed base knowledge as well 

as understanding about the potential 

value sources for the customer, on a 

very concrete level. The benchmarking 

tool presented in Figure 3 can be used 

as a sales tool for the services business 

for an entire site or for sub-processes or 

process islands. It allows a value-based 

sales process, and more specifically, the 

matching of Outotec’s service offering 

against the customer’s actual needs, as 

defined on a detailed site assessment. It 

allows a transparent sales process, which 

can be defined in close cooperation with 

the customer. Outotec will be able to 

use the tool and its results also in internal 

product development, since it will 

become more aware of the customers’ 

key challenges. Addressing the service 

product portfolio accordingly will give 

Outotec insight to what type of services 

the customers value the most. 

Summary 

There is a need for a systematic assessment 

framework for concretizing value, 

benchmarking it and ultimately optimizing 

the offered service solutions. The 

benchmarking method and tool helps 

to compare different sites according 

to their operational and maintenance 

environments. The benchmarking tool 

helps to identify and visualize the potential 

sources of value. With this approach 

based on categorizing sites to comparable 

units and benchmarking them 

against each other, the service provider 

is able to improve its capability in: 

• Showing improvement potential in 

asset management and make recommendations 

of applicable asset 

management policies, 

• Facilitating sales by optimizing the 

customer-specific product and service 

offering, and 

• Concretising customer value of the 

service provision. 

REFERENCES Komonen, Kari; Kunttu, Susanna; 

Ahonen, Toni (2011). In search of the Best 

Practices in Maintenance - New Methods and 

Research Results. Handbook 1 st International 

Maintworld Congress. Helsinki, 22-23.3.2011. 

KP-media Oy. Helsinki 2011, pp. 166-177. 

Valkokari, Pasi; Ahonen, Toni; Kunttu, Susanna; 

Horn, Susanna (2016). Fleet service solutions 

for optimal impact (in Finnish). Promaint – 

kunnossapidon erikoislehti. Kunnossapitoyhdistys 

Promaint ry, 30(1), pp. 36-39. 

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RELIABILITY 

What are you 

willing to do to 

improve 

reliability? 

With strong leadership, 

reliability can be improved, 

and every employee will 

benefit. It is very difficult to 

force change, but when 

people are motivated 

anything is possible. 

Photo Steve Potts 

JASON TRANTER, 

CMRP, Mobius 

Institute, jason@ 

mobiusinstitute.com 


AT OUR MOST RECENT CONFERENCE I had 

a very interesting discussion with an enthusiastic 

engineer who was frustrated 

with the progress made to improve reliability. 

He was primarily involved with 

condition monitoring, but he took every 

opportunity to talk to others about why 

equipment failed and what they could 

do to avoid failure. But it was rare that 

anyone took his advice. His supervisor 

was supportive, and would occasionally 

set up meetings with people to facilitate 

the discussion about reliability improvement. 

But again, very little actually 

changed… 

Does this seem familiar to you? Have 

you been trying to improve reliability 

but no one seems to take your advice? 

Do people agree that what you are suggesting 

makes perfect common sense 

but then go on doing what they have 

always done? 

I had two completely different suggestions 

for him. We need to create incentive 

and buy-in. I wonder if you have 

tried either of these. I would recommend 

trying both. 

Incentive: You need the 

support of senior management 

When suggestions for change come 

from a person who is at the same level of 

management, or below, (or from a different 

department), there is little incentive 

to change. Even if you believe that the 

proposed changes should be made, you 

are then left with extra work to do (or 

having to convince others to make the 

change), and justify the time that you 

spend pursuing those changes. But if 

that is not in your job description, if that 

is not how your performance is being 

measured, then it is unlikely you will 

spend any amount of time or effort on 

such a project. 

Therefore the directive needs to come 

from “above”. If a senior vice president, 

for example, made the declaration that 

reliability should be improved, and especially 

if people’s goals and job description 

changed as a result, then you will 

have a much greater chance of seeing 

change happen. 

Upon explaining this point I received 

the following response - a response I 

have heard many times before – “but I 

have explained the benefits of reliability 

improvement and made suggestions to 

quite senior managers, but they either 

nodded their head in agreement - and did 

nothing - or suggested I go and speak to 

someone else.” 

Ah yes, grasshopper, but how did you 

make your suggestion? (I didn’t really call 

him grasshopper.) The key is in the language 

you use and the detail you provide. 

How do you gain senior 

management support? 

As technical people we are often attracted 

to technical solutions. We can 

understand the logic. We especially like 

the common sense solutions. If we had

Successful reliability 

programs have one thing 

in common… 

SECURITY 

a strong condition monitoring team. 

As a condition monitoring 

professional or manager, you may be 

faced with starting a condition monitoring 

program or tasked to get a program back on 

track. There iLearnReliability is a lot to know; multiple technologies, 

 

monitoring practices, analysis, fault-reporting 

and trending, not to mention proper corrective and 

routine maintenance to reduce the likelihood of faults from 

reoccurring. Where can you start? Where can you learn what you 

need, that is economical in respect to time and expense? And where 

can you get training that is practical, going further than just filling you 

with facts and figures? 

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RELIABILITY 

the time, we might be attracted by the 

prospect of solving a problem. However, 

for the most part, senior leaders are not 

technical people. They are not going to 

be involved directly the implementation 

of anything you suggest. They are instead 

motivated in the areas where their 

performance is measured; revenue, cost 

reduction, risk mitigation, regulatory 

compliance, customer satisfaction, delivering 

shareholder value (or whatever 

drives your business) and perhaps other 

things. Therefore, all they really want to 

hear is how you can help them achieve 

their goals. 

Therefore, rather than discussing 

technical issues, regardless of how practical 

and sensible they may seem to you, 

you need to focus on how reliability improvement 

helps them increase revenue, 

reduce cost, reduce risk, etc. You need to 

RELIABILITY IMPROVEMENT RELIES ON DEVELOPING A 

“CULTURE OF RELIABILITY” AND ENGAGING WITH PEOPLE 

SO THAT THEY ACTIVELY CONTRIBUTE AND BENEFIT BY THE 

RELIABILITY IMPROVEMENT PROCESS. 

speak their language. You need to show 

how you can help them achieve their 

goals. And that’s how you will get their 

attention. 

There is a lot more that can be said 

about how you get their support, and 

that is for another article, but here is a 

summary: 

1. Understand what drives the business 

and how it measures success. 

2. Assess the risks faced by your organization 

(e.g. safety, environmental, production 

loss, etc.). 

3. Determine why and where the organization 

has poor reliability. 

4. Evaluate the extent to which reliability 

improvement can help close the gap 

between the current state and the desired 

state. Put a dollar/euro value on that gap. 

This is an investment after all. 

5. Evaluate the extent to which reliability 

improvement can help minimize the risks 

faced by your organization. If possible, put 

a dollar/euro value on the risk mitigation. 

6. Implement one or more pilot projects, 

and measure their effect, so that you can 

prove that it can work in your organization. 

7. Establish a business case that demonstrates 

the value of reliability improvement 

which is supported by the results achieved 

in your pilot projects. Don’t provide technical 

information unless requested. Make it 

clear how the goals of the senior management 

team can be achieved which includes 

mitigation of risk. 

Are you willing to do that? 

Upon making this suggestion I saw his face 

go pale. Senior management? Business case? 

Investment? Company goals? Pilot projects? 

It was a daunting prospect… 

Unfortunately, I was making life 

very difficult for him. I wanted to 

give him a simple solution - but over 

30 years of involvement with these 

programs, I have not seen anything 

else work. People need an incentive to 

change. Senior management is in the 

best position to create that incentive. 

Of course, they need to understand 

how each individual employee will 

benefit, but regardless, they need an 

incentive to do anything. 

Buy-in: Allow people to take 

ownership of the ideas 

If I came to you and suggested you 

do something differently, how will 

you react? Will you think that I am 

implying that you have been doing 

something wrong? Will you see it as 

more work you have to deal with? Will 

you have any buy-in, or ownership, of 

the process? What level of personal 

motivation will you have to make those 

changes; especially if you do not clearly 

understand the benefits? 

Sure, if I was your supervisor, and I 

required you to make a change you may 

make it, reluctantly. But unless I was 

very clear about the priorities, and I 

followed up with requests for progress 

reports, the change may not be made. 

What if we instead engaged in a 

conversation related to a goal you are 

trying to achieve or problem you are 

trying to solve? What if, during that 

discussion, you came to the conclusion 

that a change should be made? 

And what if you were in a position to 

take ownership of that change, and you 

knew that you would be recognized for 

taking the initiative and helping your 

coworkers and the business? 

Would you be more likely to make 

the change? 

If an organization sees the urgent 

need to improve reliability, and everyone 

has a thirst for making improvements, 

then suggestions from others 

are more likely to be taken on board 

and implemented. But if there is no 

push from above, and you do not see 

how you personally benefit, and there 

is no accountability, then there is very 

little reason to make any improvements 

whatsoever. 

Reliability improvement relies on 

developing a “culture of reliability” and 

engaging with people so that they actively 

contribute and benefit by the reliability 

improvement process. When it 


RELIABILITY 

is a win-win, change will happen and 

the improvements will be sustained. 

If not, frustration will continue. 

Are you willing to do that? 

Upon making that suggestion I could 

again see some doubt. Technical 

people are not always great with the 

“touchy-feely” stuff. We may be interested 

in technical solutions, and 

we might want to dictate how things 

should be done and maybe even take 

credit for the change. That motivates 

you, but it doesn’t motivate the other 

person. So this is a tough decision to 

make. What’s most important, you 

or the people who are working with? 

What’s most important, you or the 

success of the reliability improvement 

program (i.e. the success of the 

organization)? 

Conclusion 

With strong leadership, reliability 

can be improved, and every employee 

will benefit. It is very difficult to force 

change, but when people are motivated 

anything is possible. 

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Backlog management has a 

number of different but interdependent 

focuses: Backlog 

Work Order Quality, Age of 

Backlog and Backlog Size Management. 

This article will focus 

on Backlog Work Order quality. 

Later editions of Maintworldmagazine 

will cover Time in 

Backlog and Backlog Size Management 

in more detail. 

Effective 

Backlog 

Management 

STEVE GILES, 

Marshall Institute, 

sgiles@ 

marshallinstitute.com 

WHILE A MAINTENANCE backlog is critical 

to an effective Planning and Scheduling 

process, it can be viewed negatively 

by some groups or individuals. 

In reactive organizations, putting 

a work order in backlog is viewed as a 

negative action. The assumption is made 

that the work order has been tossed 

into a black hole, never to return to the 

light of day. Unfortunately the nature 

of a reactive maintenance effort causes 

this belief to be correct: work orders 

only emerge from backlog when the 

condition the task is addressing has deteriorated 

to the point where it must be 

handled as an urgent or emergency work 

order. That, in turn, causes most work 

requests to be initially prioritized as 

urgent or emergency - piling more fuel 

on the fire-fighting nature of a reactive 

maintenance programme. 

In truth, the primary purpose of a 

maintenance backlog of the Planning 

and Scheduling process is to allow the 

maintenance planner adequate time 

to plan, order and receive materials 

and services before the task is placed 

on a schedule for execution. Without 

this window of time, most work will be 

scheduled before all the waste in the task 

has been removed, and in many cases 

before all the required material is on site. 

This results in an inefficient task at best, 

but also encourages the reuse of faulty 

parts resulting in a short-term repair. 

Early and accurate identification of 

maintenance tasks is key to providing 

this planning time. This early identification 

combined with realistic priority 

setting helps to bring credibility to the 

Planning and Scheduling process. 

A maintenance backlog is also used 

in some organizations to maintain a correctly-sized 

maintenance staff by crew. 

Backlog Work Order Quality 

The maintenance backlog has several 

different stages: 

Awaiting Planning – newly converted 

work requests awaiting the planning 

process 

In Planning – work orders the planner is 

actively planning 

Awaiting Materials or Services – planned 

and estimated work orders awaiting 

delivery of special ordered materials or 

for a specialty contractor to commit to a 

requested time of execution 

Ready to Schedule – work orders that are 

fully planned with all materials on the 

site and any contract or services needed, 

committed to the required timing 

Successfully managing the quality 

of work orders in the different stages of 

the backlog begins with the creation of 

the work request. A well-written work 

request will result in a well-written work 

order. A poorly-written work request will 

require someone (normally the planner) 

to investigate what the maintenance task 

really consists of before it can be converted 

to a work order for planning. 

Failure to address the quality of work 

requests entering the work order system 

will reduce the effectiveness of the planner 

and lead to at least some of the work 

orders not being adequately planned. 

Lack of a well-planned work order will 

reduce the accuracy of the estimates. 

Inaccurate work order estimates lead to 

over or understating the amount of real 



man-hours held in backlog. That inaccuracy 

will hinder the effective scheduling 

and the overall credibility of the entire 

planning and scheduling process. 

Shop Floor Training 

These problems can be addressed easily 

by providing shop floor training 

for everyone involved in creating the 

work request. Training combined with 

a clear setting of expectations by floor 

supervision and ongoing coaching by 

everyone involved in the work order 

process will lead to clear accurate work 

requests. 

In a world-class process, the requestor’s 

supervisor would be the first 

reviewer to quickly provide coaching on 

inadequate work requests. By addressing 

the inadequate work request at this 

point, not only will the work request be 

corrected at the source, but the clear 

expectation of creating a quality work 

request will be quickly established. 

The process flow shows the tight 

loop between the requestor and their 

supervisor, ensuring all information 

on the work request is adequate before 

sending the work request to the work 

request review meeting. 

A multifunctional team (primarily 

Maintenance and Operations supervision 

or management) reviews all open 

work requests prior to the start of the 

day. This is normally a very short meeting 

either approving the work request 

and sending it to the planner, or rejecting 

the work request. 

The process flow details the review 

process each work request is given before 

being submitted to the planner for 

conversion into a work order or deletion. 

(Some CCMS’s do not allow the work 

request to be deleted, but place a flag on 

the work request so that it cannot receive 

charges.) 

This review will minimize duplicate 

work requests and prevent the creation 

of duplicate work orders, incorrectly 

prioritized work requests, invalid work 

requests, and work requests assigned to 

the wrong queue. 

Monitor Open Work Requests 

A good metric to encourage an effective 

work request process is to monitor 

open work requests by age. Work 

requests should have a very short life 

– 24 hours to possibly 96 hours. Any 

work request older than 24 to 96 hours 

indicates a dysfunctional work request 

process. 

Work request training should be given 

to everyone in the organization expected 

to create work requests. World 

Class practices are to have everyone 

responsible for identifying and documenting 

maintenance tasks identified 

during their normal daily duties. The 

cost of CMMS access can sometimes 

cause this responsibility to be restricted 

to a smaller number of personnel. 

If that is the case, a companion system 

should be developed (hard copy or electronic) 

to expand the responsibility to 

identify maintenance tasks as broadly 

as possible. 

It is important that the work request 

training be formally documented to ensure 

quality work requests, regardless 

of personnel turnover. 

In parts 2 and 3, the Age of the Backlog 

and Backlog Size Management will 

be discussed in detail. 

Discover 

the hidden 

treasure in 

Maintenance 

Discover 

the hidden 

treasure in 

Maintenance 

There is value hidden in every maintenance organization. All companies have the potential to further improve, either by reducing 

costs, improve safety, work on the lifetime extension of machinery or by smart maintenance solutions that improves uptime. The 

question is where maintenance managers should be looking to fi nd these areas of improvement and where they need to start. 

You will fi nd the answer to this question at Mainnovation. With Value Driven Maintenance ® and the matching tools like the VDM 

Control Panel, the Process Map and our benchmark data base myVDM.com, we will help you to discover the hidden treasure in 

your company. 

Do you want to discover the hidden treasure in your maintenance organization? 

Go to www.mainnovation.com 

CONTROLLING MAINTENANCE, CREATING VALUE.


Figure 1. 

Bearing Condition Monitoring 

Using Ultrasound 

Airborne & structure-borne ultrasound has become a major player in bearing condition 

monitoring. Once considered just a leak detector, more maintenance & reliability 

professionals are beginning to realize all of the benefits associated with using ultrasound 

for condition monitoring applications. The P-F Curve, with which we have all 

become familiar, reflects that trend. 

ADRIAN MESSER, 

CMRP, adrianm@ 

uesystems.com 

THE I-P-F CURVE shows Ultrasound as 

being the first technology that detects a 

failure that is mechanical in nature such 

as early stage bearing wear, or subsurface 

bearing fatigue (See Figure 1.) 

It has been said that at least 60 percent 

of premature bearing failures can be attributed 

to lubrication, whether it’s over 

lubrication, under lubrication, use of 

the wrong grease for the wrong application, 

or use of a contaminated lubricant. 

Ultrasound instruments can be used 

to prevent over and under lubricated 

bearings. The source of ultrasonic noise 

is friction; when a bearing is in need of 

grease, there is an increase in friction and 

therefore an increase in noise or decibel 

level. When listening to the bearing that 

is in need of lubrication and watching the 

decibel level on the display of an ultrasonic 

instrument, as grease is applied the 

inspector would notice a gradual drop in 

the decibel level, eventually back down 

to a more normal level. If the bearing is 

already over lubricated, as soon as grease 

is applied, the inspector would notice a 

gradual increase in the decibel level, letting 

them know that the bearing already 

had enough grease. 

Figure 2. PUMP 3 MTROB 007 Figure 3. PUMP 4 MTROB 010 


3/2017 maintworld x


How Do I Get Started? 

There are two common questions that 

many first-time users of ultrasound 

have. The first is, “How do I set baselines?” 

The second is, “How do I know if 

what I’m listening to is good or bad?” 

The Comparison Method 

One way to get a quick idea as to what is 

good and what is bad is by using the comparison 

approach. With this method, the 

inspector simply compares the decibel 

level readings at identical points on 

identical machines. Using this method, 

the inspector also begins to “train” 

their ear as to what rotating equipment 

sounds like, and it will become obvious 

that a bearing with a particular fault 

such as an inner race, or outer race defect, 

will sound much different than a 

bearing that is in a “good” condition. 

The baseline can then be set based on 

an average of decibel levels at the compared 

points. The software may even 

default to the first reading taken and 

downloaded. The baseline can then be 

changed as more readings are collected. 

The Historical Method 

The historical method is the preferred 

method for establishing baselines and 

alarm levels in bearing condition monitoring 

routes. Using this method, the inspector 

first establishes a route or database 

in the ultrasound software. The database 

is then loaded into the ultrasonic 

instrument. Data is then collected at the 

various points along the route. When the 

initial round of data has been collected, 

it may be necessary to collect data more 

frequently than needed in order to build 

Figure 4. Pump 4 MTR OB from the ultrasound instrument. 

Notice the distinct 175.8Hz harmonics detected. 

the history, and get an idea if the decibel 

readings are remaining similar in the 

historical readings. 

For example, when collecting the 

initial data for setting the baseline, the 

readings may need to be taken once per 

week for 4-5 weeks. Once the baseline is 

set, the readings need only be taken only 

once per month, or every other month 

depending on asset criticality and equipment 

runtime. 

Ultrasound Imaging 

Through advancements in ultrasound 

instruments and software, the user can 

obtain an “image” of the sound that is 

being heard to analyse, diagnose, and 

confirm mechanical fault conditions in 

rotating equipment. 

Examples of 

Ultrasound Imaging 

Let’s take a two motor and pump combination 

as an example: 60hp motors 

powering water pumps. 

While collecting data, both decibel 

readings and sound files were recorded. 

In Figures 1 and 2 screen shots from the 

spectral analysis software show a comparison 

between the points “PUMP 3 

MTROB 007” and the “PUMP 4 MTROB 

010.” 

Notice the difference between the 

two points. Both motors are operating 

under the same conditions, but the 

Pump 4 MTR OB point has a much different 

spectrum. If you were listening 

through the headset of the ultrasound 

instrument, it would also have a much 

different sound. 

Another image of the Pump 4 

MTROB point, captured from on board 

the ultrasound instrument, can be seen 

in Figure 3. 

The spectrum analysis software used 

has a built-in bearing fault frequency 

calculator. By entering in the speed 

(rpm) and the number of balls (bearings), 

an outer race, inner race, ball pass, 

and cage frequency are calculated. For 

this particular motor, the speed was 

1750rpm and the type and number of 

WITH A SHORT LEARNING 

CURVE, EASE OF COLLECT- 

ING DATA, AND REMOTE 

MONITORING SOLUTIONS, 

ULTRASOUND CAN BECOME 

ANOTHER VALUABLE TOOL TO 

USE FOR YOUR CONDITION 

MONITORING EFFORTS. 

bearings was confirmed and the number 

of bearings was 10. The fault frequency 

calculated by the spectrum analysis software 

that was of interest was an inner 

race fault at 175Hz. This is the same fault 

harmonic detected on the ultrasound 

instrument. Another interesting point 

was the fact that the vibration analysis 

data was collected two days later, and did 

confirm an inner race fault on the Pump 

4 motor outboard point. 

Conclusion 

Implementing ultrasound for condition 

monitoring applications is easier 

than you think. With a short learning 

curve, ease of collecting data, and remote 

monitoring solutions, ultrasound can 

become another valuable tool to use for 

your condition monitoring efforts. 

Lubrication PM’s can also become 

more effective because ultrasound 

trends will show which bearings need 

to be lubricated. Therefore, instead of 

greasing everything on a time-based lube 

route, only the points that are currently 

in the lubrication alarm from ultrasound 

trends are greased until the decibel level 

drops back down to the baseline dB. 

If you are only using ultrasound as a 

leak detector, I would encourage you to 

take a more in depth look into condition 

monitoring with ultrasound. 


28. International Exhibition 

for Electric Automation 

Systems and Components 

Nuremberg, Germany, 28 – 30 November 2017 

sps-exhibition.com 

Answers for automation 

Electric Automation and Digital Transformation 

Your free entry ticket 

sps-exhibition.com/tickets

CONDITION ADVERTORIAL MONITORING 

Auto Correlation Simplifies 

Vibration Analysis, and Enhances 

Efficiency of Rotating 

Machinery Maintenance 

Vibration analysis is one of the most successful 

techniques for monitoring the condition of rotating 

equipment, but unless you are a vibration specialist 

the information can often be difficult to decipher. 

How can peak value analysis and auto correlation 

help improve maintenance efficiency? 

VINCENT BURSON 

Emerson 


MISALIGNMENT, gear defects, insufficient 

lubrication, pump cavitation and rolling 

element bearing defects are all problems 

associated with rotating machinery that 

result in increased vibration. Vibration 

analysis is therefore one of the most 

important techniques for monitoring 

the condition of such machines as part 

of a predictive maintenance programme. 

The periodic and, where appropriate, 

continuous collection of vibration data 

enables potential problems to be identified 

earlier. This helps to prevent unexpected 

failures that can cause safety incidents 

and production loss. Maintenance 

can be scheduled at appropriate periods 

of downtime. The benefits of vibration 

analysis are widely recognised in terms 

of reduced maintenance costs and the 

increased safety and plant efficiency it 

helps to provide. However, with a shortage 

of experienced plant maintenance 

engineers, companies often do not have 

personnel with the necessary ability to 

correctly interpret the often-complex 

vibration data available. 

Vibration analysis relies on data collected 

from vibration sensors monitoring 

the rotating equipment. This data 

can be collected manually and periodically 

using handheld vibration analysis 

devices. Alternatively, equipment critical 

to production is often monitored 

on a continuous basis (often referred 

to as online monitoring) to ensure that 

changes that may indicate a potential 

problem are not missed in between manual 

rounds. Online monitoring systems 

also often incorporate protection functionality 

that helps to bring equipment 

to a safe state (offline) should an issue be 

identified. 

Signal processing 

In general, the analogue signal from a vibration 

sensor is routed via an analogue 

signal processor, converted into a digital 

format and then further processed digitally. 

The output of the vibration sensor 

is expressed in g units, and the signal 

processing may include the conversion 

of the signal to velocity units. The 

analogue signal (in g or velocity units) is 

usually passed through a filter immediately 

before being converted into a digital 

format, providing assurance that the 

digital representation of the analogue 

signal is correct. 

By far the most common form of signal 

processing for analysing vibration 

from rotating equipment is the Fourier 

Transform. This uses a fast Fourier 

transform (FFT) algorithm to enable the 

signal to be converted and to construct 

the spectrum either in acceleration or 

velocity units. This spectral analysis is 

helpful in separating the band-limited 

signal into periodic components related 

to the turning speed of the machine. 

Standard spectral analysis is the traditional 

method used to gain insight into 

machinery problems that create vibration, 

but its complexity makes it difficult 

for anyone who is not a specialist vibration 

engineer to analyse and interpret 

the data. In contrast, the peak value 

analysis (PeakVue) methodology introduced 

by Emerson to help analyse vibration 

data has proven to be very effective, 

presenting the information in a way that 

makes it easier for personnel other than


PEAK VALUE METHODOLOGY HAS PROVEN TO BE A 

VERY USEFUL TOOL FOR VIBRATION ANALYSIS IN ROTATING 

EQUIPMENT APPLICATIONS WHERE NORMAL SPECTRAL 

ANALYSIS HAS PROVEN TO BE LESS EFFECTIVE. 

vibration specialists to interpret and 

identify problems. 

Peak value analysis 

Peak value analysis technology provides 

a simple, reliable indication of equipment 

health via a single trend - filtering 

out traditional vibration signals to focus 

exclusively on impacting faults, where 

metal parts come into contact with each 

other. 

In this method, peak values are observed 

over sequential discrete time intervals, 

captured, and then analysed. The 

analyses are: 

A. the peak values (measured in g’s). 

B. spectra computed from the peak value 

time waveform. 

C. the auto correlation coefficient computed 

from the peak value time waveform. 

All three analysis tools enable the 

defect, and often its severity, to be identified. 

As a measure of impacting, peak 

value analysis readings are much easier 

to interpret. A healthy machine that is 

correctly installed and well lubricated 

shouldn’t have any impacting. This establishes 

the zero principle: the peak value 

measurement on a healthy machine 

should be at, or close to, zero. 

As common machinery faults begin to 

appear on rotating equipment, the peak 

value reading typically can be evaluated 

using the so-called Rule of 10’s. 

This applies to rolling element bearing 

machines operating between 1000 and 

4000 rpm. It simply states that when the 

peak value levels reach 10, there is some 

problem with the machine; when they 

double to 20 there is a serious problem; 

and when they double again to 40 there 

is a critical problem (see Figure 1). 

Rule of 10’s example 

As an example of how the Rule of 10’s 

operates, let’s consider a typical process 

pump running at between 900 and 4000 

rpm as it passes through the four stages 

of bearing failure before progressing to 

machine failure. 

STAGE 1 -The defect is not visible to the 

human eye and there is no change in the 

overall vibration, but peak value analysis 

already provides an indication that 

something is happening. When the peak 

value rises to a value of 10, this indicates 

that there is a problem with the bearing. 

STAGE 2 - Small pits begin to appear 

and the bearing has less than 10% of its 

service life remaining. Typically, overall 

vibration still does not provide an 

indication of the developing faults, but 

the peak value level continues to climb. 

When it doubles to 20, this indicates a 

serious problem with the bearing. 

STAGE 3 - the bearing damage is now 

clearly visible. You may start to see a 

small increase in overall vibration of +/- 

10 percent. Meanwhile, the progression 

in fault severity is obvious using peak 

value analysis. 

STAGE 4 - the overall vibration may rise 

by 20 percent or more. In comparison, 

the peak value level continues to increase 

sharply – perhaps as high as 40 

g’s – and signals that the bearing is approaching 

the end of its life. 

MACHINE FAILURE - there will be a 

marked increase in the overall vibration 

at the point of actual failure, but too late 

Figure 1. Operators with no special 

training in machinery diagnostics can use 

peak value analysis measurements to 

determine both when a piece of rotating 

equipment is healthy and when an 

abnormal situation is present. 

to support planned maintenance. This is, 

in effect, notification that the machine 

is shutting down. In contrast, peak value 

analysis has been indicating a developing 

fault over the past weeks and months. 

Immediately prior to failure, peak value 

levels may surge rapidly to 50 g’s or 

higher. 

Operators with no special training in 

machinery diagnostics can use peak value 

analysis measurements quickly and 

easily to determine both when a piece of 

rotating equipment is healthy and when 

an abnormal situation is present. Once 

an abnormal situation has been identified, 

detailed diagnostic information 

can be extracted from the peak value 

analysis waveform or spectrum to determine 

the exact nature of the defect. This 

method can be used to visualise distress 

signals on a machine that are simply not 

visible with other vibration measurements. 

Earlier indication of developing 

defects facilitates optimum maintenance 

planning and minimises the impact on 

production. 

Auto correlation 

Auto correlation is a time domain analysis, 

computed from the peak value time 

waveform that is useful for determining 

the periodicity or repeating patterns of 

a vibration signal. The auto correlated 

waveform can be presented in a circular 

format, which makes interpretation of 

the data much more straightforward. 

On the following page are some examples 

of how vibration analysis data 

can be viewed, using standard spectrum 

analysis, time waveform, auto correlation, 

and finally auto correlation in a 

circular format. 

Conclusion 

Peak value methodology has proven to 

be a very useful tool for vibration analysis 

in rotating equipment applications 

where normal spectral analysis has 

proven to be less effective. Using auto 

correlation and circular displays, problems 

can be easily identified without 

vibration analysis experience. This helps 

to simplify maintenance tasks, enabling 

a greater number of devices to be effectively 

monitored. Previously difficult to 

identify problems will be quick and simple 

to diagnose at an early stage, helping 

repair work to be scheduled, preventing 

machinery failures, reducing overall 

maintenance costs and improving plant 

safety and efficiency. 


CONDITION ADVERTORIAL MONITORING 

ANALYSIS COMPARISONS 

STANDARD SPECTRUM ANALYSIS: 

TIME WAVEFORM: 

When using standard vibration 

monitoring to monitor problematic 

gearboxes, the levels in the vibration 

spectrum do not appear to be that high 

- 0.5 mm/sec. Looking at the spectrum 

you can see increasing harmonics of 

the suspected gear mesh frequency. 

To trained personnel, the gear shows 

potential signs of misalignment. However, 

to anyone who is not a vibration engineer, 

this means very little and can be difficult 

to analyse. 

Using the time domain, the difference in activity levels is more apparent. The high levels 

and activity in the top waveform is a huge contrast to the lower time waveform. Modulation 

can be seen in the top waveform, but to anyone other than a vibration engineer, this doesn’t 

mean a great deal. Note that the scales on both plots are the same at 18g’s. 

AUTO CORRELATION: 

Comparison gearbox 

Noisy gearbox 

When the time signal is auto correlated, it produces a value of between 0 and 1. When it 

is close to 0 it means the signal consists of random frequencies, which indicates a lack of 

lubrication. When close to 1, that shows that there are repeatable signals, such as impacting, 

indicating a broken gear tooth or a failing bearing. You can see that there is modulation in 

the signal on the noisy gearbox and the readings are close to 1. Even to a layman, diagnosis 

of the gearbox is easy. 

Top: healthy gearbox clearly showing 

lack of gear mesh frequencies. Bottom: 

problematic gearbox showing harmonics 

of the gear mesh frequency. This diagram 

compares vibration readings that were 

taken on two gearboxes – one healthy and 

one problematic. Note the lack of gear 

mesh frequencies in the healthy gearbox 

compared to the other. 

Comparison gearbox 

Noisy gearbox 

By showing the auto correlated waveform in a circular format, the difference becomes 

obvious and the misalignment of the gear can be clearly seen. In this case, the misalignment 

was due to mismatched gears. 


Master the Language 

of Your Machinery 

A4300 VA3 Pro 

3-Channel Vibration Analyzer, Data Collector 

and Many More 

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Stroboscope 

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Adash 

Hlubinská 1379/32 

702 00 Ostrava 

Czech Republic 

tel.: +420 596 232 670 

+420 596 232 687 

fax.: +420 596 232 671 

info@adash.com 

www.adash.com 

Your local distributor can 

be found on our website 

www.adash.com under 

‘Contacts‘.

P R O V E N Q U A L I T Y 

QUALITY MEANS TO US: 

TO PROVIDE PREMIER 

SOLUTIONS FOR 

PLANT MAINTENANCE. 

• Laser shaft and geometric alignment 

• Portable vibration analysis and field balancing 

• Online Condition Monitoring 

• Worldwide training, services and support 

INNOVATION MADE IN GERMANY. 

Find out how we boost uptime. 

Made in Germany 

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Qualified Support 

Quality Service 

PRÜFTECHNIK Dieter Busch AG 

Oskar-Messter-Str. 19-21 

85737 Ismaning 

Tel.: +49 89 99616-0 

info@pruftechnik.com 

pruftechnik.com

Maintworld 3/2017

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