Guidelines to Understanding to estimate MTBF

IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 3, 2013 | ISSN (online): 2321-0613
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Guidelines to Understanding to estimate MTBF
Narendra D. Chauhan1
Prof. Nilesh H. Pancholi2
1
M.E.[CAD/CAM] student 2
Faculty of Mechanical department
1, 2
Department Mechanical Engineering
1, 2
INDUS Institute of Technology and Engineering (IITE), Ahmedabad, Gujarat
Abstract— To quantifying a reparable system or reliability
we can use MTBF. It has been used for various decisions.
MTBF is determining the reliability. For developing the
MTBF model we can use Poisson distribution, Weibull
model and Bayesian are the most popular approach. In this
paper we are talking about complexities and
misconceptions of MTBF and clarify in sequence what
are the items and concerns that need to be consider in
estimating MTBF.
I. INTRODUCTION
To every industries to operates at lower cost, bigger profit
and continuously to meet customer satisfaction MTBF
plays a big role. MTBF mainly implemented for to
planned maintenance regime instead of unplanned
maintenance or reactive maintenance regime. Reactive
maintenance seen to be one of the factor contributes
to high manufacturing cost. MTBF study can be used as
a bottom line for further improvement that needs to be
done and as a monitoring tool to monitor the success of
the improvement implemented. MTBF can
synchronization the maintenance time to avoid frequent
shutdown especially in a single-stream process. In a single-
stream process, every shutdown due to whatever reasons
seen as a maintenance opportunity and it is necessary for
maintenance personnel to know what are the value can be
added to the losses. The definition of MTBF based on
failures and assumptions, which need a proper
interpretation and well, kept in record. Therefore, this
study will clearly interpret all the related components in
MTBF. It is the most common means of comparing
reliabilities, the factor of meaningless MTBF results are
unclear failure definitions, misinterpretation and
unrealistic estimations. Field data measurement uses field
failure data produces more accurate results than
simulations. New design product or low volume
productivity of product may not have sufficient field's
data, thus field data measurement is more accurate.
II. DEFINITIONS
Availability is an ability of a component to be in state to
perform a required function at a given instant of time or at
any instant of time within a given to time interval,
assuming that the external resources, if required, are
provided.
Dependability is the ability to deliver service that can
justifiably be trusted.
Reliability is an ability of a component to perform a
required function under given conditions for a given time
interval.
Fault is adjudge or hypothesized cause of a system
malfunction.
Error is a deviation from the correct service state for a
system or a subsystem.
Failure is a transition event that occurs when the
delivered service deviates from the correct service state
to an unwanted state.
Failure rate is the frequency with which an engineered
system or component fails.
III. FAILURE AND ASSUMPTIONS
The definition of failure is actually infinite it is not limited
to below two. Realistic assumptions have to be considered.
To simplify the process of estimating MTBF assumption
are required. It is almost impossible to collect the correct
data and calculate the exact number. Assumptions may
come from past experience, journals, hand book or proved
previous similar project.
1) To perform its required function whole failure
system is require.
2) Failure of any individual system (subsystem) to
perform its required function but not to the system as
a whole.
IV. ESTIMATION OF MTBF AND PREDICTION
MTBF and service life is two different things. Service life
can expressed as expected number of operating hours
before system fails. While MTBF describe as:
MTBF = MTTR + MTTF
Fig. 1: Bathtub curve illustrate consistent rate of
Failures

(IJSRD/Vol. 1/Issue 3/2013/0029)
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522
MTBF consist of mean time to repair (MTTR) and mean
time to failure (MTTF). To focus at the right angle we
have deep understanding of product behavior. While
product still in their "useful life" or "normal life" will
gives high MTBF as a result. It is because; at this period
it's experiencing the lowest and almost a constant failure
rate... There should be no coloration between MTBF and
service life. A product can be extremely high MTBF
(reliability) but a low service life. See Fig. 1.
Product is in use in useful life period the field with
a product quality and results to a constant failure rate with
respect of time. Source of failures at this stage could
include:
1) Undetectable defects.
2) Low design safety factor.
3) Higher random stress than expected
4) Human factors.
5) Natural failures.
There are two ways to determine MTBF.
Reliability Prediction Method (Predict MTBF)
It is usually performed early in the product
lifecycle and based on the system design values are
calculated. For example; new product designs. If field data
does exist, this method should not be use.
Reliability Estimation Method (Estimate MTBF)
The method is to calculate value based on observe
sample of similar system. It could be done with large
population of sample deployed in the field. It is most
widely used as the product tested under a real working
environment.
Determining MTBF by using estimate method is
commonly use. MTBF is often confused with mean time to
failure (MTTF), which applies to replaceable rather than
repairable unit. The biggest challenge in implementation of
Estimate MTFF is time. High reliability product will takes
longer time. MTBF can be estimated in shorter time. Run
the test with the population of units until long enough to
have reasonably large number of failures. Replace the
failed unit in the test population with a new unit. MTBF
approximation can be obtained by multiplying number of
units in the population by the total time, and dividing by
the total failures. For the better approximation of actual
MTBF larger number of failure are require.
For a repairable system, run a small number (as
small as one) of units until they have experienced a
number of failures (repair each failed unit and put it back
into the test). Then take the total running time and divide
by the number of failures. Formula for calculating the
MTBF is (Scott Speaks, Vicor Reliability Engineering):
Total Time (T)
MTBF = -------------------------------
Number of failures (R)
For calculating the MTTF is:
Total Time (T)
MTTF = -------------------------------------
Number of units under test (N)
V. LIMITATION OF MTBF AND MTFF
The main issue that deviate MTBF results is aging. Each
component has its special age-related degradation mode.
During repair Wear and tear effects at components are
commonly detectable. Various effects, such as corrosion,
slowly take their roll. As these phenomena goes on, unit
will begin to fail at increasing rate where they have passed
the useful lives. See Fig. 1.
For the success of the MTBF estimation realistic
assumptions are always there. Annual failure rate (AFR),
has two scenarios that need to be considered (American
Power Conversion, White Paper #112, 2004). Scenario 1
makes the following 2 assumptions: (1) the products
operate 24 hours a day, 365 days a year; (2) all the
products in the populations begin at the same time. It is
relevant for products that are continuously running.
Scenario 2, for products is known to run intermittently.
VI. MTBF IMPROVEMENT
Jacob and Sreejith (2008) introduced minimum mean time
between failures (MMTBF) that is another approach to
improve MTBF. The improvement does not mean the
improvement of MTBF results. It is the way of
determining value of improvement that sufficiently
needed. This mean MTBF will be further improve by two
key components; a specified MMTBF and a maximum
acceptable probability of premature failure, Pj. Pf is the
probability that the time to failure smaller than MMTBF.
This approach is to determine the value of reliability that
need to be improve. The value can be decided based on the
control chart. The control chart also can be used to monitor
improvement in reliability for necessary action to be taken
if the improved system is not reflecting the desired result.
The control chart must have upper control limit (UCL),
central limit (CL) and lower control limit (LCL).
Assuming false alarm probability a.
VII. REDUCE REACTIVE (UNPLANNED)
MAINTENANCE
To avoid costly and unnecessary breakdowns, or to keep
process equipment running condition at target efficiently,
In Plant Asset Management (PAM), there are three main
characteristics to generate planned work in time:
1) Potential problems are identified before they effect the
process;
2) The health of the plant assets is assessed using a
combination of products and services; and,
3) Maintenance activity is driven by problem severity,
potential causes, and operator options.
VIII. RESEARCH METHADOLOGY
Plant's MTBF Base Line
MTBF situations will use for improvements.
Type of Improvement
There are few types of improvement:

(IJSRD/Vol. 1/Issue 3/2013/0029)
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523
1) Improvement on components MTBF by:
1) Improvement on component design
2) Replace to high-reliable product
3) System adjustment
2) Sufficient number of spares Instrumentation:
For complex engineering design improvement Finite
Element Analysis (FEA) will be use.
1) Data Collection
Historical data for parts replacement or
maintenance will be drill out from Maintenance register.
2) Limitation of Study
This study is limited to single streamed Titanium
Dioxide manufacturing, which uses Sulfate process
IX. CONCLUSION
Reactive maintenance is one of the main pulling factors that
limit the organization from being best-cost-producer. Its
affect the organizations in many ways. Annual
manufacturing cost, customer satisfaction and company’s
image are put into jeopardize if the organization’s
maintenance mode is not been shifted from reactive regime
to predictive
REFERENCE
[1] Scott Speaks. Reliability and MTBF overview. Vicor
reliability engineering, 1-10.
[2] National Conference on Postgraduate Research (NCON-
PGR) 2009 1st October 2009, UMP Conference Hall,
Malaysia © Centre for Graduate Studies, University
Malaysia Pahang Editors: M.M. Noor; M.M. Rahman
and K. Kadirgama

Guidelines to Understanding to estimate MTBF

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