Maintworld 3/2017


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


maintenance & asset management

Using Technology and

Innovation to Manage



p 28


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maximize your machine uptime doesn’t stop there. Moog Industrial Services provides

products, services and total support that match your specific O&M needs and

machinery challenges.

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Rely on high-quality servo repair

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©2016 Moog Inc. All rights reserved.

Dear friends,

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

Societies (EFNMS, 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


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,, having

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

the Salvetti Foundation, it is giving four maintenance awards (

Finally, EFNMS is a partner of the European Agency

for Safety and Health at Work (OSHA, 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 ( 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



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



Backlog management has

a number of different but

interdependent focuses:

Backlog Work Order Quality,

Age of Backlog and Backlog Size



Benchmarking allows

a company to compare

its own practices

and processes to the

practices applied in

the best firms of the

industrial branch.



Revolutionize Your Business

with AI and Machine Learning:

The Productivity Boost of the


Developing Leadership in

Maintenance and Reliability

How to Identify the Root Cause


of a Misalignment Condition


Enjoy Success with Small


IIoT Simplifies Predictive


Maintenance Solution

Deployment and Maintenance

Bearing Grease Replenishment -


On-Condition or Time-Based?

Using Technology and


Innovation to Manage Mega-

Maintenance Challenges

Elements of a Good Preventive


Maintenance Program



Demonstrating Value with


What are you willing to do to

improve reliability?

Effective Backlog Management


Bearing Condition Monitoring


Using Ultrasound


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,, Editor-in-chief

Nina Garlo-Melkas tel. +358 50 36 46 491,, Advertisements Kai Portman, Sales Director, tel. +358 358

44 763 2573, Subscriptions and Change of Address members, non-members Printed by Painotalo Plus Digital Oy, Frequency 4 issues per year, ISSN L 1798-7024, ISSN

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

3/2017 maintworld 5


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



Bentley Systems,


x 6 maintworld 3/2017


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







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


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



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


• Classification – Classification is

typically used for data that can

be categorized, such as whether

an email can be classified as

genuine or spam.


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


• Neural Networks – A rule-based

computer system modelled on

the human brain’s processing


3/2017 maintworld 7


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


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 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




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


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.

8 maintworld 3/2017

APM Solutions to

Keep You on Target




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.

10 maintworld 3/2017



Senior Instructor

Eruditio, LLC,



CMRP CAMA, Partner

Eruditio, LLC,


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







3/2017 maintworld 11


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


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


along. This is someone who will show

you what to do differently and work with

you when you get stuck using a tool or


Fourth, constantly advocate for additional

training, for you and your


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


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?

12 maintworld 3/2017

Why gamble

with Reliability


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

How to Identify

the Root Cause of a

Misalignment Condition


Senior Project



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.


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


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







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.

16 maintworld 3/2017







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.


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

18 maintworld 3/2017



Managing Director,

Adash Ltd.,

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


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.












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


The first step deals with regular

measurements and monitoring the

3/2017 maintworld 19


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


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


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


Electrical defect

generates vibrations


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


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



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


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.



20 maintworld 3/2017

The Uptimization Experts.

What does


mean to you?


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.


President of Beeond,




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 ( Organization

for Machine Automation and

Control ( and The Association

for Manufacturing Technology (AM- 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


Users Spend an Inordinate

Amount of Time and Money

on Solution Integration and


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).

22 maintworld 3/2017


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


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


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


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


Beeond, Inc. ( 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


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


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


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


• 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

3/2017 maintworld 23


Bearing Grease Replenishment -

On-Condition or



Director of Business

Development for SDT

International, allan@

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 - 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?

24 maintworld 3/2017


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


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


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


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.

3/2017 maintworld 25

Road Map to Operational

Readiness and Asset


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© 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.

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Learn more at

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Using Technology and Innovation to




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



Services Manager for

Wind at Moog,


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


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


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


28 maintworld 3/2017


Inside a wind turbine

hub, the pitch system

adjusts the angle of

the rotor blades.




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,

3/2017 maintworld 29


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


• 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.





30 maintworld 3/2017

Be a LUBExpert



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Elements of a

Good Preventive



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.



Founder and CEO of

IDCON INC., Raleigh


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


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.

32 maintworld 3/2017


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


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


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


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



Go to the next




“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?


Can they be

trained in > x





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


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

3/2017 maintworld 33



Value with


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.


VTT Technical Research

Centre of Finland Ltd.,



VTT Technical Research

Centre of Finland Ltd.,


Outotec Oyj, susanna.

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,

34 maintworld 3/2017

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


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.


Benchmark among similar


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


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


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


Figure 3. User

interfaces of the


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

36 maintworld 3/2017


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


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.


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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What are you

willing to do to



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


CMRP, Mobius

Institute, jason@

38 maintworld 3/2017


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


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…


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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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





speak their language. You need to show

how you can help them achieve their

goals. And that’s how you will get their


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


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


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

40 maintworld 3/2017


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



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.





Marshall Institute,


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


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

42 maintworld 3/2017


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


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


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


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.


the hidden

treasure in



the hidden

treasure in


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, 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



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.


CMRP, adrianm@

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

44 maintworld 3/2017

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


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


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









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.


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.

46 maintworld 3/2017

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Nuremberg, Germany, 28 – 30 November 2017

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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?



48 maintworld 3/2017

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


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






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


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


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


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.


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.

3/2017 maintworld 49





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.


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.

50 maintworld 3/2017

Master the Language

of Your Machinery

A4300 VA3 Pro

3-Channel Vibration Analyzer, Data Collector

and Many More


Expert system


Optional modes:





Run Up



Hlubinská 1379/32

702 00 Ostrava

Czech Republic

tel.: +420 596 232 670

+420 596 232 687

fax.: +420 596 232 671

Your local distributor can

be found on our website under







• Laser shaft and geometric alignment

• Portable vibration analysis and field balancing

• Online Condition Monitoring

• Worldwide training, services and support


Find out how we boost uptime.

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