This simple equipment reliability example is used to convey the basics of reliability engineering. Learn the 4 system reliability factors that must all be addressed. Download the full 235 slide Reliability and Maintenance Management Course PowerPoint today at https://bin95.com/articles/maintenance-management/equipment-reliability-examples.htm

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If maintenance is one quarter of the solution to high reliability, what are the others?
The discussion on managing reliability continues…
The first is good design control, the second is accurate manufacture/assembly, and third
is good operating practices.
But I cannot influence those factors, I can only affect the maintenance practices.
Then you will never get high reliability in your operation. All four factors must all be addressed
together. The parts in your machines do not care about your organizational structure and how
responsibilities are allocated to people. The parts can only respond to how they are designed,
how they are fabricated and installed, how they are treated and maintained.
I hear what you are saying Professor. I will need to bring this perspective back to my company.
Perhaps it is best that tomorrow I explain the basics of ‘reliability’ and give you simple
examples of how it is measured. You will then see the importance of the four critical factors –
design control, manufacture, operation, maintenance – to equipment lifetime reliability.
Thank you Professor, I’ll see you at 11am tomorrow.

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What is Reliability?
Reliability is the probability an item will function
correctly when needed, for the period required, in
the specified environment.
The reliability of a
part, or of a whole
machine, or
system in service
depends on:
1. Robustness of the
Design to Lack of
Manufacturing
Precision
2. Precision and
Accuracy in
Manufacture and
Assembly
3. Roughness of
Loading in
Operation
4. The Quality,
Relevance and
Timing of
Maintenance
Probability is the same
as ‘chance’
In other words for a
set amount of time
How reliable is this glass?
This means what is the
chance that this glass will
hold water the next time you
want to use it? If this glass
is broken, then it will not be
available to you to use – it
will have failed.
That is, it properly performs
the job it is meant to do

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What is the Reliability of this Glass?
In other words: ‘What’s the chance it will hold water next time you use it?’
Stay with me, because
understanding how to
measure reliability is one
of the most important
concepts that you need
to know of to do
maintenance well.
What can cause this glass to break?
• It can be dropped, for example -
• slip from your hand
• fall off a table
• slip out of a bag or carry box
• It can be knocked,
• hit by another glass
• clanked when stacked on each other
• hit by an object, like a plate or bottle
• It can be crushed,
• jammed hard between two objects
• stepped-on
• squashed under a too heavy object
• It can be temperature shocked,
• in the dish washer
• during washing-up
• Mistreated,
• It can be thrown in anger
• It can be smashed intentionally
• Latent damage
• scratched and weakened to later fail more easily
• chipped and weakened to later fail more easily
These many ways for
the glass to break (the
failure mode), are called
‘failure mechanisms’.

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How do you Measure Reliability of a Glass?
Since reliability is the
‘chance’ that the glass
will still be usable, we can
measure reliability ‘in
reverse’ by working out
the chance it will fail.
Because we know the ways the glass can fail (its failure mechanisms), we can
estimate it’s chance of failing (i.e. being broken – which is its failure mode) in a
period of time. What is left over is the chance of not failing – its Reliability.
Time or Service
Chance
of
Failing
Failure
Reliability
t1

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Each ‘x’ mark is a failure at that time.
What can cause this glass to break?
• It can be dropped, for example -
• slip from your hand
• fall off a table
• slip out of a bag or carry box
• It can be knocked,
• hit by another glass,
• clanked when stacked
• hit by an object, like a plate or bottle
• It can be crushed,
• jammed between two objects
• stepped-on
• It can be temperature shocked,
• in the dish washer
• during washing-up
• Mistreated,
• It can be thrown in anger
• It can be smashed intentionally
• Latent damage
• scratched and weakened
• chipped and weakened
Measuring the Number of Failures
2
0
Time - Years
0 30
Number
of
Failures
by
each
Mechanism
2
0
2
0
2
0
2
0
2
0
20
10
x x
x x
x
x
x
x
x
x
x x x
x
x x x
x x
x
x
x x
x

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What can we say about the lives of
glasses in this household?
• With 24 glasses broken in 30 years, the
average failure rate, or failure frequency,
per year is 24/30 = 0.8 glasses a year
• The Mean Time To Failure (MTTF) for a
glass is 1/0.8 per yr = 1.25 years i.e.
glasses last on average 1-1/4 years
before one is broken
Measuring the Rate of Failures
2
0
Time - Years
0 30
Failed
Glasses/year
2
0
2
0
2
0
2
0
2
0
20
10
x x
x x
x
x
x
x
x
x
x x x
x
x x x
x x
x
x
x x
x
5
Use Mean Time To Failure (MTTF) to describe
failure of a single part, and Mean Time
Between Failure (MTBF) to describe failure of a
machine, which is a collection of parts.
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We need to convert rate
of failure to chance of failure.
• If 25 glasses were always in use in the
household (glass replaced after each loss)
• Chance of Failure per year
= Breakages per Year
Total Number of Glasses
= 0.8
25
= 0.032 (or 3.2%)
Converting to ‘Chance of Failure’
Time - Years
0 30
Number
Remaining
0
20
10
25 24
23
21
19
18
16
15
14
13
12 11
9 8
7
5
3 2
1
We have ‘averaged’ the failure rate at 3.2 glasses for every 100, i.e. a glass broken every 1.25
years. In doing so we have lost the real truth of the situation. Look at years 3-4, and 25-26-
27-28 there were no breakages, but we have ‘assumed’ a regular rate of failure of a glass every
1.25 years. We must be careful measuring ‘chance; it is never certain. The best we can do is
depict what is likely to happen, but there will always be a degree of uncertainty that it will.

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Drawing the Failure Curve for a Glass
Time - Years
0 30
Chance
of
Failure
20
10
100%
Averaged ‘Failure’ Plot for
Glasses in that Household
Time - Years
0 30
Chance
of
Being
Useful
0
20
10
100%
Averaged ‘Chance of Being
Useful’ Plot for Glasses in
that Household
Usefulness = 1 – Chance of Failure
= 1 – 0.032
= 0.968 (say 97%)
3.2%
Actual
Breakages
This failure rate of the glass reflects:
• Robustness of the Design
• Quality (Precision and Accuracy) in
Manufacture
• Roughness and Care during Use
• The Quality of Maintenance Care
96.8%
Once we know a glass’
chance of failure, we also
know the chance it will be
there when we go to use it.

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No Maintenance for Random Failures
Time - Years
0 30
Chance
of
Failure 20
10
100%
3%
For parts where the first sign of failure is
the breakage that destroys it, there is no
point replacing the part until it breaks
because there is nothing wrong with it
until it is failed (by us). We have to carry
spare parts for such parts because there
is no degradation curve to monitor.
Like a drinking glass, many electronic parts
exhibit ‘random failure’ and suddenly fail
without warning. But we now know that the
failure was is not the parts’ fault – we
humans did it by overloading it!
Breakage of
drinking glasses is a
‘random failure event’
Electronic parts
failure is mostly a
‘random failure event’
We don’t actually know
when the glass will fail!
The past history of broken
glasses lets us calculate an
‘average’ rate of failure
Time - Years
0 30
Number
Failed
0
20
10
25 24
23
21
19
18
16
15
14
13
12 11
9 8
7
5
3 2
1
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Random Failures are Preventable
We saw that failures occur because of situational overload or material degradation leading to
fatigue. Overload is caused by poor operating practices where people mistakenly impose too much
stress on a part, or the part’s designer selected the wrong material, or misunderstood the part’s
service duty. Material degradation is a local environment cause, and that can be controlled.
X X
Both causes of ‘random failure’ are controllable by us.
We can prevent overloading and we can manage
the local environment that a part ‘sees’.
Probability
Overload Condition Fatigue Condition

12. From the failure history you were able to get a mathematical model predicting the future.
They discuss the example…
Not actually predicting the future. It’s more like estimating the chance a thing will happen.
How accurate is the model for use in maintenance strategy decision making?
This was a very simple example intended to show you that the life of parts can be estimated with
some level of confidence, especially over long periods of time. Reliability engineering is not an
exact science, rather it provides evidence for making more certain risk management decisions.
It’s very impressive Professor, can you give me more examples?
Yes, tomorrow let’s have a look at the failures of some other common parts we use
often, but these will have different failure curves to those seen so far.
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