Simple guide to MTBF – What it is and when to use it

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Simple guide to MTBF –
What it is and when to use it
Erik Hupjé
P R E V E N T I V E M A I N T E N A N C E
www.reliabilityacademy.com

Contents
3 Overview
3 What is MTBF?
4 Failure rate
4 Reliability
5 The history of MTBF
5 How to calculate MTBF
6 Life expectancy of equipment
6 Service life
6 Mission life
6 Useful life
7 MTBF versus MTTF
7 MTBF versus MTTR
8 What is reliability prediction?
9 What MTBF is not
10 When not to use MTBF
10 When to use MTBF
11 Conclusion
11 References

3
Reliability Academy | Simple guide to MTBF – What it is and when to use it
Mean Time Between Failure (MTBF) is one of the most widely recognised and yet least under-
stood indicators in the maintenance and reliability world. Manufacturers quote it as a rating of
their products and industry uses it as a measure of success. But there is so much misunder-
standing associated with MTBF that there is even an online movement to abandon MTBF. In
this article, I will explain in simple terms what MTBS is, what it’s not, when to use and when not.
It is said that the great Greek philosopher
Socrates argued that “the beginning of
wisdom is the definition of terms.”
Socrates would have been unimpressed with
our use of MTBF or would have challenged
our collective wisdom when it comes to MTBF.
Sure, there are clear definitions for MTBF.
But, unfortunately, there is a lack of common
understanding of what MTBF really means.
So, let’s start with the definition:
MTBF stands for Mean Time Between
Failures and represents the average time
between two failures for a repairable
system.
Overview
What is MTBF?
For example, three identical pieces of equip-
ment are put into service and run until they
fail. The first system fails after 200 hours, the
second after 250 hours and the third after
400 hours. The MTBF of the systems is the
average of the three failure times, which is
283.33 hours.
Let’s look at some of the definitions of crit-
ical terms related to MTBF. MTBF is related
to failure rate. It assumes a constant random
failure rate during the useful life of a piece of
equipment.
But what do these terms really mean? We
need a clear set of definitions so that we
understand what an MTBF number is telling
us and what the limitations of that number

4 Reliability Academy | Simple guide to MTBF – What it is and when to use it
are. There is even a movement to abandon
MTBF because of the misunderstanding and
misuse of the term.
We can learn more about MTBF by exploring
its origin and the reasons why it came into
use. It also helps to compare MTBF with other
indicators to avoid confusion about terms.
This article covers all these aspects along with
some clear guidance about where to use and
not to use MTBF.
Failure rate
The failure rate is the number of failures in
a component or piece of equipment over a
specified period. It is important to note that
the measurement excludes maintenance-re-
lated outages. These outages are not deemed
to be failures and therefore, do not form part
of this calculation. A failure rate does not
correlate with online time or availability for
operation – it only reflects the rate of failure.
Failure Rate = No. Of Failures / Time
In industrial applications, the failure rate
represents past performance based on histor-
ical data. But in engineering design, the failure
rate can also be predicted. It is common to
use a bathtub curve to illustrate failures over
the entire life of a product.
There is a high rate of infancy failures at the
beginning of its life and a high rate of wear out
failures at the end of its life. But in between,
during the product’s useful life, its rate of
failure is expected to be reasonably constant.
Manufacturers seek to reduce infancy failures
by testing products and removing early fail-
ures before they get to the customer.
The disadvantage of failure rate as an indi-
cator is that it yields a tiny result, which is diffi-
cult to interpret. The failure rate of a pump
could be 0.4 or even orders of magnitude
lower than that.
Reliability
Before World War II, the term reliability
described how repeatable a test was. The
more repeatable the results, the more reli-
able the test, whether it be in the field of
mechanics, psychology or any other scientific
endeavour. However, the challenges of World
War II caused new developments in the defi-
nitions and engineering associated with reli-
ability.
Electronics equipment during the war was
highly problematic. Up to half of the electronic
equipment on a naval vessel could be out of
service at any time – leading to a renewed
focus on understanding and improving equip-
ment reliability. Working groups developed
strategies like setting quality and reliability
standards for electronic equipment suppliers.
The Advisory Group on the Reliability of
Electronic Equipment (AGREE) came up with
the classic definition of reliability:
“The probability of a product performing
without failure a specified function under
given conditions for a specified period of
time.”
Around this same time, studies showed that
up to 60% of failures in army missile systems
were related to component reliability. Military
and commercial aviation continued to drive

5
improvements in reliability engineering
throughout the twentieth century.
The most commonly used reliability predic-
tion formula is the exponential distribution,
which assumes a constant failure rate (i.e. The
flat part of the bathtub curve).
Reliability = e ^ (-failure rate x time)
Engineers report reliability as a percentage. It
indicates the probability of failure for a piece
of equipment in the time given. Reliability
does not predict when the equipment could
fail during that time, but only the chance of
that failure occurring at any point during the
time given.
We calculate MTBF by dividing the total
running time by the number of failures during
a defined period. As such, it is the inverse of
the failure rate.
MTBF = running time / no. of failures
During normal operating conditions, the
chance of failure is random. It could happen at
any time on the flat part of the bathtub curve,
just as easily as it could at any other time.
Using the exponential distribution for reli-
ability calculation, the MTBF then represents
the time by which 63% of the equipment has
failed. I.e. Only 37% of components are still in
service.
The history of MTBF
The MTBF calculation comes out of the
reliability initiatives of the military and
commercial aviation industries. It was intro-
duced as a way to set specifications and stan-
dards for suppliers to improve the quality of
components for use in mission-critical equip-
ment like missiles, rockets and aviation elec-
tronics. The military handbook containing
MTBF information for electronics Mil-HDBK
217 is discontinued, but other resources like
The Telcordia still make use of the military
handbook.
Maintenance practitioners first used MTBF
as a basis for setting up time-based main-
tenance strategies. Inspection intervals and
routine maintenance tasks were set up based
on MTBF. These programs aimed to identify
potential failures before they occurred, but
time-based systems are not the most effec-
tive strategy. Condition monitoring is one
example of a strategy that is far more effec-
tive for predicting failure than time-based
programs based on MTBF.
How to calculate MTBF
As mentioned in the definition, MTBF is calcu-
lated by dividing the total time by the number
of failures. Let’s look at a few examples:
Assuming a situation where there are 1,000
cars that run for one year. If one car fails in
that time, the MTBF would be:
MTBF = (1 yr x 1,000 cars)/1 failure = 1,000
years per failure
In an unusual case, consider the MTBF of
human life, assuming a population of 500,000.
If during the course of a year, 625 people died

of random causes, the MTBF would be:
MTBF = (1 yr x 500,000 people)/625 deaths =
800 years per death
This example highlights where MTBF could be
misleading as no human being expects to live
for 800 years.
In a population of 500 ANSI pumps in water
service across multiple sites, 600 fail in a
period of three years. The MTBF would be:
MTBF = (3 yrs x 500) / 600 failures = 2.5 years
per failure
On their own, these numbers provide some
information about reliability but not enough
to fully understand the reliability performance
of the equipment.
Life expectancy of
equipment
Every equipment has a life expectancy based
on its components, its design, operating
conditions and maintenance history. But not
everyone is talking about life expectancy in
the same way when they use the term. The
service life, the mission life and the useful life
of a piece of equipment all refer to different
things. We can unpack those differences in
more detail.
Service life
Service life refers to the entire duration of
an equipment’s use. We measure it from the
time of commissioning to its final failure or
decommissioning.
Engineers also predict service life based on
the design specifications. A service life predic-
tion would typically be used in calculations
to justify the capital expense of a new asset.
Actual service life can be compared with the
design service life of a piece of equipment to
determine whether it met the expectations of
engineers when it was first purchased.
One unique example is that of a missile. By
nature, we expect a very high MTBF for a
missile indicating the very low probability
of failure. But the service life of a missile is
very short. It can be as little as a few minutes
from the time a missile is fired to the time it
explodes.
Mission life
Mission life is the duration used for reli-
ability calculations and analysis. For example,
we base the failure rate calculation on the
number of failures in a specific time. This time
is known as the mission life.
Engineers use reliability indicators to predict
failures and make decisions about the future
mission life of their equipment. This includes
making decisions about spares holding or
maintenance strategies for a mission life of
the next five years.
Useful life
Useful life refers to the flat part of the bathtub
failure curve. It leaves out the time associated
with infancy failures at the beginning as well
as the time associated with wear out failures
at the end of a product’s life. Useful life is,

7
therefore, the operational life of any piece of
equipment.
In design terms, it reflects the maximum life
expectancy of any equipment during normal
operations. The useful life does not take into
account operating conditions or maintenance
history – it assumes a constant and random
failure rate.
MTBF versus MTTF
Mean Time To Failure (MTTF) is closely related
to MTBF. The difference between the two is
that MTTF applies to non-repairable systems,
while MTBF applies to repairable systems.
In other words, the MTTF calculation is as
follows:
MTTF = service time / no. of failures
Engineers determine MTTF by observing a
large number of identical components and
their combined service time. In this way,
it gives some indication of the probability
of failure. It is an important indicator for
complex systems where some parts cannot
be replaced but could impact on the MTBF of
the system as a whole.
A fan belt in a motor is a typical example.
Fan belts should have an MTTF that is higher
than the MTBF of the equipment into which
it fits. Otherwise, the whole equipment may
fail when the fan belt fails. This correlation
provides a key for improving an engineering
design. The way to improve MTBF of a complex
system may be to purchase better quality
parts that have a higher MTTF performance.
Nevertheless, one must always bear in mind
that MTTF and MTBF are probability related
and do not guarantee the life of a piece of
equipment up to that duration.
MTBF versus MTTR
Mean Time To Repair (MTTR) describes the
average time to execute a repair on the equip-
ment over a given period. It is calculated by
adding together the total time for repairs and
then dividing by the number of failures during
that period.
MTTR = total repair time for all repairs / no. of
failures
This acronym could also describe the Mean
Time To Recovery, which is slightly different.
When using recovery as the basis, the time
added must include the notification time of
maintenance tasks. In other words, besides
the repair time, there is additional time to
diagnose the fault and plan the repair. Using
recovery as the basis for the calculation gives
a higher result than using repair time alone.
MTTR does not give enough information
on its own to improve maintenance perfor-
mance. Reasons for the duration must be
investigated to determine whether the time
to repair can be reduced. Strategies to reduce
repair times may include spares holding strat-
egies or developing in-house skills instead of
relying on outside contractors.
Lengthy repairs have the potential to cause a
loss in production. Where this is the case, the
losses are usually much more significant than

the cost of the repair itself. Loss of production
adds a significant economic incentive to mini-
mise the MTTR of mission-critical equipment.
MTTR is different to MTBF. Having both results
available gives more information to engineers
than either one gives on its own. Equipment
that fails regularly but is quick to repair needs
a different reliability solution to equipment
that hardly ever fails but takes a long time to
repair.
What is reliability
prediction?
Reliability prediction is an attempt to estimate
the failure rate of a complex product made
up of several components. It comes from the
field of electronics, and this is where it is most
often applied.
Electronics manufacturers use empirical
handbooks for reliability prediction using
MTBF. These books offer predicted MTBF
for different electronic components based
on field failure rates with some simplifying
assumptions. But the handbooks are usually
conservative in their estimates and ignore
differences in the application design, which
could influence failure rate significantly.
Manufacturers use the component MTBF
data to calculate an estimated MTBF of their
product made up of multiple components –
this is known as reliability prediction.
But the limitations of using the handbooks
and their assumptions must be taken into
account when using predicted reliability infor-
mation. Predicted reliability is most useful for
comparative purposes. For example, a manu-
facturer could compare the predicted MTBF
of different components to help them choose
the most appropriate component for their
product.
There are two main methods of reliability
prediction, with one variation included:
• The parts count method uses the failure
rate of the various components as well
as the count of components to calculate
a failure rate for the product itself. It is a
theoretical exercise and can only be veri-
fied once the product is in service, and an
actual failure history is established.
• The parts stress method uses actual field
information from large numbers of the
component operating within its rated
conditions. Engineers use this historical
data as a base for predicting the failure
rate of products sold in the present. Of
course, field information is not available
when a new component comes onto the
market. Therefore, some manufacturers
use a modified version of the parts stress
method known as the accelerated life test-
ing method.
• The accelerated life testing method seeks
to establish failure statistics for a product
by placing it under high stress, for exam-
ple, operating a component at a higher
temperature higher than its rating. These
extreme operating conditions cause
premature component failure. Engineers
use this failure information to back-cal-
culate predicted reliability under normal
operating conditions.

9
Different electronic handbooks use different
assumptions and choosing one over the
other could lead to considerable differences
in MTBF prediction. Comparing MTBF calcu-
lations using one set of assumptions with
an alternative calculation based on different
assumptions is meaningless. On the other
hand, using the same base assumptions to
compare components or designs is more
helpful.
What MTBF is not
There is some opposition to the use of MTBF
as a reliability indicator. Proponents of this
view have gone to the extent of creating a
movement called “nomtbf”. There is a website
of that name and several resources that argue
that MTBF is not useful as a reliability indi-
cator or even misleading. Let’s consider some
of the objections.
1. People commonly mistake MTBF as an
expected life of a piece of equipment
before failure. The first part of the indi-
cator – “Mean Time” give the impression
that on average, each equipment should
last at least this long. But MTBF is based
on a probability distribution where the
expected failure rate is constant. The
resultant exponential distribution gives a
result of almost 63% failure by the MTBF
value. In other words, only 37 % of equip-
ment remain operational by the time
they reach their MTBF.
2. In cases of extreme misunderstanding,
some people mistake MTBF as the mini-
mum expected time between failures.
This mistaken view leads to significant
disappointment because 63% of equip-
ment have already failed by then.
3. MTBF offers no information about the
cause of failures. Therefore, it does
not yield any insights about what could
prevent the failure from reoccurring.
Only a root cause analysis can deliv-
er this additional and highly valuable
information for improving reliability
performance. Failures are not random
in practice. They are caused by operat-
ing conditions that differ from design
conditions, the quality of maintenance,
the quality of spares used in repairs and
human error – to name a few. Eliminating
causes of failure is a significant contribu-
tor to improving reliability performance,
but MTBF does not contribute to that
vital process.
4. The same MTBF result can mean very
different things from an equipment reli-
ability perspective. For example:
5. If you have 1,000 cars each driving one
mile, and one of those cars fails – you
get an MTBF of 1,000 by dividing the
total miles by the total failures. On the
other hand, if you get a single car driving
1,000 miles during which it fails once,
you also get an MTBF of 1,000. These are
quite different scenarios, and they reflect
different reliability performance, but
yield the same MTBF.
6. MTBF assumes a random and constant
failure rate – the flat portion of the
bathtub curve. The assumption is
simplistic and does not reflect realworld

conditions. Many pieces of equipment
have an increasing probability of fail-
ure, the longer they operate. A different
probability distribution would give a
better correlation with real-world condi-
tions and would, therefore, provide more
meaningful information from a reliability
perspective.
Misunderstanding MTBF can lead to poor
business decisions that are costly to organi-
sations. Using MTBF without additional infor-
mation about the causes of failures and how
to predict failures fails to take advantage of
the multiple tools for maintenance and reli-
ability available to engineers. Rather than
build a maintenance strategy on a theoretical
constant rate of failure, maintenance practi-
tioners can build their strategy around current
condition monitoring results and predictions
of failure.
When not to use MTBF
MTBF should not be used when the bathtub
curve does not represent the actual failure
rate. If the component has a wearing part,
which increases the chance of failure over
time, then MTBF will not accurately describe
the probability of failure. In this case, MTBF
over-predicts failures early in the equipment’s
life and under-predicts failures the later part
of its life.
The best approach for deciding whether to
use MTBF is to first establish the reasons
behind the need for this information. For
example, if the need is to set spares holding
requirements, then there may be a better
approach or more information required to
make that decision. If the need is to estimate
the expected mission or service life of a piece
of equipment, then MTBF is not the right tool
for that task.
When to use MTBF
In my opinion, it is not necessary to throw out
MTBF completely as a maintenance and reli-
ability indicator. We need to understand its
limitations and its benefits and use it as one of
many tools that help us improve the reliability
of equipment in our area of responsibility.
Some ways that we can use MTBF include the
following:
MTBF is a great way to compare similar equip-
ment operating in similar conditions in terms
of performance. A Waterworld article3 high-
lights this point. The article quotes an average
MTBF of 2.5 years for an ANSI pump. Poor
performance for this pump is 1.5 to 2 years
MTBF, and excellent performance is more
than 4 years.
Maintenance and reliability practitioners can
use this information to evaluate the perfor-
mance of their equipment. If their ANSI pump
falls into an acceptable range, they may turn
their attention to other equipment that could
benefit from more direct intervention. But if
their pump is performing poorly, it gives them
the motivation to investigate the reasons why
and come up with corrective measures.
Another good use of MTBF is to monitor prog-
ress in reliability initiatives. It is a lagging indi-
cator meaning that the current MTBF result
reflects the effectiveness of past actions.

11
Once a reliability program is implemented –
like condition monitoring, risk-based inspec-
tion or other RCM strategies, it is crucial to
measure the impact of that program.
Over time, equipment should become more
reliable, and therefore, MTBF should increase.
If there is no noticeable change in MTBF, then
the reliability program is not achieving its
objectives. A positive trend of MTBF over time
for equipment on site gives maintenance and
reliability practitioners confidence that their
programs are achieving the desired results.
However, reliability initiatives may take some
time to reflect in the lagging indicators like
MTBF.
MTBF is also useful for engineering design.
Engineers use MTBF in electronic manufac-
ture to compare the effect of using different
components in an electronic product. It also
helps identify design weaknesses. There may
be one component that lowers the MTBF of
the product as a whole, and a single change
could make a significant impact on design
reliability. Electronic manufacturers choose
components that meet their overall MTBF
objective. Over-specifying components adds
to the cost of the product, but under-spec-
ifying could lead to premature failures and
customer dissatisfaction.
When using MTBF information for design, it
is important to understand the parameters
of the manufacturer’s claims. If MTBF from
one manufacturer covers a broader range of
operating conditions, it may not be directly
comparable with figures quoted from another
source.
Conclusion
In this article, we have explored the idea of
MTBF – its origins, the misunderstandings
people have about its meaning and the ways
it is used and abused.
While there is a movement to abandon the use
of MTBF completely, it does serve a purpose
when its limitations are understood and when
used in conjunction with other information.
MTBF is a helpful tool for comparative
purposes. It used to evaluate different design
options and make choices about compo-
nents. During the service life of a piece of
equipment, it can be used to compare perfor-
mance against other similar equipment in
similar service. This comparison helps main-
tenance and reliability practitioners to make
wise decisions about where to use their time
and energy. Lastly, it can be used as a lagging
indicator to evaluate the effectiveness of reli-
ability programs like condition monitoring
and risk-based inspection.
References
1. History of Reliability Engineering, James
McLinn, American Society for Quality –
Reliability and Risk Division, https://www.
asqrd.org/home/history-of-reliability/
2. Reliability Engineering Principles for the
Plant Engineer, Drew Troyer, Reliable Plant.

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