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1
SYNOPSIS
of the Ph. D. thesis entitle
Investigation on Performance Reliability Improvement
by Optimizing Maintenance Practices through Failure
Analysis in Continuous Process Industry
Submitted in
Partial fulfillment of the degree of
DOCTOR OF PHILOSOPHY
of the
GUJARAT TECHNOLOGICAL UNIVERSITY, AHMEDABAD
by
Pancholi Nilesh Hasamukhlal
(Enrolment No:-129990919010)
Supervisor
Dr. M. G. Bhatt
GUJARAT TECHNOLOGICAL UNIVERSITY
CHANDKHEDA, AHMEDABAD
November 2017
2
1. Title of the thesis and abstract
1.1 Title
Investigation on Performance Reliability Improvement by Optimizing Maintenance Practices
through Failure Analysis in Continuous Process Industry
1.2 Abstract
The proposed research work addresses the reliability and maintenance issues of major process
industries. The work deals with the comprehension of failure pattern; reliability modeling and
discrimination of the critical components through substantial shop-floor failure data. These
data are collected for the period of April-2013 to March-2014 for aluminium wire rolling mill
plant situated at Ahmedabad, India. The research work narrates a method for evaluating risk
priority number (RPN) and maintainability criticality index (MCI) through traditional as well
as multi-criteria decision making (MCDM) approaches respectively for each failure cause of
identified critical components. There are three non-identical MCDM approaches discussed
namely; technique for order preference by similarity to ideal solution (TOPSIS), grey-
complex proportional risk assessment (COPRAS-G) and preference section index (PSI).
The primary findings of this research work are to prioritize the maintenance activities by
comparing results obtained through different failure analysis models. It is proposing
improvements in the maintenance plan of critical components like; bearings, gears, and shafts
of aluminium wire rolling mill which are commonly representing the most critical
components in a large range of industrial processes.
Originality mainly consists in the contemporary application of non-identical MCDM methods
(TOPSIS, COPRAS-G and PSI). It will help to elucidate maintenance issues of major process
industries and recommended deliverable keys where multi-criteria decision-making (MCDM)
approaches are very useful.
2. Brief description on the state of the art of the research topic
The reliability analysis issues were discussed almost half century back. These issues are
considered as useful in the field of reliability modeling, risk analysis and maintenance
planning. Barlow and Proshan (1965, 1975) researched some practices in maintenance
activities. Dekker (1996), Pham and Wang (1996), and Jensen (1995) discussed the
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classification of maintenance models. Sikorska (2008) presented the scope to improve the
quality of failure histories stored in computerized maintenance management systems.
Looking to the literature review; it seems many researchers have done various modifications
for improvement of FMECA to overcome drawbacks for different processing plants. Hwang
and Yoon (1981) highlighted the importance of MCDM, where multiple and conflicting
criteria are under consideration in different area like personal, public, academic or business
contents; Gilchrist (1993) incorporated failure cost to his modified FMECA model;
Bevilacqua et al. (2000) presented modified FMECA through Monte Carlo simulation in
power plant; Braglia (2000) developed multi-attribute failure mode analysis with economic
considerations; Xu et al. (2002) presented FMEA of engine system based on fussy assessment
concept; Braglia et al. (2003) presented fuzzy TOPSIS FMECA to overcome the limitations
of conventional US MIL-STD-1626A method; Sahoo et al. (2004) showed that FMECA is a
basic part of the maintenance plan and a strong tool to evaluate and improve system
reliability with reduction of overall maintenance cost; Sachdeva et al. (2009) presented a
multi-criteria decision-making approach to prioritizing failure modes for paper industry using
TOPSIS; Gargama and Chaturvedi (2011) presented risk factors in fuzzy linguistic variables
to generate fuzzy rank priority number; Maniya and Bhatt (2011) presented the multi-criteria
decision-making method to solve problems of facility layout design selection based on
preference selection index (PSI) method; Adhikary and Bose (2014) presented multi-factor
FMECA through COPRAS-G method for coal-fired thermal power; Fragassa et al. (2014)
presented an advanced application of FMECA used in integration with other quality tools
(FTA, RDA) for recognizing critical functions on diesel intake manifold in a view to
optimizing industrial processes where several parts are realized in aluminium (including
wires). Mobin et al. (2015) proposed an integration of a fuzzy analytic hierarchy process
(FAHP) and the complex proportional assessment of alternatives to grey relations (CORPAS-
G) to prioritize suppliers in an Iranian manufacturing industry; Zhang (2015) deduced
closeness coefficient for failure modes by integrating both subjective and objective weights to
avoid over or under estimation though fuzzy TOPSIS; Chanamool and Naenna (2016)
highlighted the importance of Fuzzy FMEA to prioritize and assess failures associated with
working process of hospital’s emergency department. Mittal et al. (2016) described the
ranking of major problems of plywood industries through multiple-attribute decision-making
(MADM) approach based fuzzy TOPSIS. Rathi et al. (2016) presented fuzzy MADM for
prioritizing six sigma projects through fuzzy VIKOR in the Indian auto sector. Rastegari et al.
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(2017) addressed condition-based maintenance and its implementation with vibration
monitoring techniques in order to plan maintenance activities of the spindle units of the
automobile gear box manufacturing company in Sweden. Farley and Miller of Innoval
technology Ltd. presented three parts on maintaining rolling mill performance. In first part
they identified some factors responsible for unhealthy rolling mill performance over time. In
second part they discussed the overall equipment efficiency (OEE) based approaches to avoid
such decline in order to improve rolling mill performance and in third part they explained
guidelines to avoid initial decrease in performance of new mill through good design, training
and technical support.
Many researchers have presented modified FMECA approaches to various industries, but
quality research is lacking in the aluminium wire rolling mill which forms an important
segment of the process industry. Literature review shows that past researchers have not yet
considered the case of three multi-criteria decision-making approaches simultaneously
applied to any process industry. There is a huge scope for improvement in reliability by
optimizing maintenance practices through failure analysis based on different MCDM
approaches in aluminium wire rolling mill plant.
3. Definition of the Problem
After globalization, various process industries all over the world face the problem of keeping
the subsystem and components in efficient working condition for carrying out its designated
functions effectively for a sufficient possible longer time. Minimum downtime of
components is the pressing needs of major process industries such as the rolling mill, dairy
plant, chemical-petrochemical plant, sugar mill, textile mill, paper industry, fertilizer plant
etc. Reliability oriented maintenance is relatively a new tool for mechanical engineering in
India to addresses reliability issues in process industries. It analyzes the system and sub-
system of plant and tries to find out the failure modes, effects and consequences of the
failure. Also, the study can be at preventing or reducing such failures. The growing need for
higher reliability arises from the requirement to develop the maintenance plan which
continuously performs in the most efficient manner possible.
Out of various process industries as stated earlier, the aluminium wire rolling mill is selected
for study because aluminium transmission wire market size was about 6.5 lacs Metric Tons in
volume terms in financial year (FY) – 2015 in India (IEEMA’s 68th
Annual Report – 2014-
15). It is likely to grow at a compound annual growth rate (CAGR) of 13.5% between FY14-
19 due to inter-regional transmission network expansion, infrastructure, industrial demand
5
and Government of India’s “power for all” initiative (Indian Electrical Equipment Industry
Mission Plan 2012 – 22). In Gujarat also transmission lines network is likely to grow at a
CAGR of 7.8% between FY14-18 and Government of Gujarat will be investing US$4.6
billion in transmission and distribution by 2020 (BIG 2020 – Final Report Volume 1 –
Summary and Vision, August 2009).
During the detailed study of performance of identified aluminium wire rolling mill, it is
observed that approximately 20 to 25 % of possible production time goes towards
maintenance of equipment i.e. loss of reliability. After studying the facts related to aluminium
wire rolling mill plant, it is found that maintenance is the main cause of low productivity and
profit. Hence, it is required to upgrade current control practices associated with maintenance
system with a view to increasing the effective utilization of resources with little cost or
without any additional cost.
Literature reviews state that FMECA is widely used and accepted tool to enhance
maintenance practices in process industries. It is based on the systematic brainstorming
session to recognize the failures which may occur in system or process (Vandenbrande,
1998). It is devoted to determining the design reliability by considering the potential causes
of failures and their effects on the system under study (Dhillon, 1985 and O’Conner, 2002).
In present research study, the problem is defined for the scope of investigating the extent at
which the reliability of aluminium wire rolling mill can be improved by ameliorating current
control and maintenance practices of aluminium rolling mill through three distinct MCDM
based FMECA approaches with traditional approach:
(i) Technique for order preference by similarity to ideal solution (TOPSIS) where; scores
are considered in weighted crisp value
(ii) Grey-complex proportional assessment (COPRAS-G) where; weighted scores are in
grey number range rather than in crisp value and;
(iii)Preference selection index (PSI) where; subjective weight consideration not required.
4. Objective and Scope of work
The main aim of this study is to enhance existing maintenance practices with modified
FMECA through MCDM approaches of identified aluminium wire rolling mill and to derive
the scope of improvement in maintenance strategy to process industries of similar or different
kinds in accordance with failure analysis.
The research objectives explored in this study are as under:
6
(i) To study reliability and maintenance issues faced by process industry deriving the
scope of maintenance optimization.
(ii) Collection and analysis of historical failure data including evaluation of major
reliability parameters for components of identified aluminium wire rolling mill.
(iii)Identification of the critical components based on their downtime, frequency of failure
and loss of production in terms of volume and cost.
(iv) Understanding the failure modes, causes, effects, consequences of failure pattern and
present maintenance practices of identified critical components and assignment of
scores to each failure causes based on real shop-floor condition for further failure
analysis models.
(v) Optimizing maintenance activities of critical components through traditional as well
as multi-criteria decision-making (MCDM) approaches to prove its competency.
Scope of Work
The scope of proposed research work is summarized as below:
(i) Understanding the scenario of the performance of different process industries and to
acquire relevant information about reliability and maintenance issues. Study existing
maintenance practices and its limitations, deriving scope of improvements.
(ii) Collection of historical failure data. (The data set can be used for comparison and
investigation of future failures with the highest probability of occurrence). Failure
data analysis and estimation of reliability parameters to generate necessary inputs.
(iii)Discrimination of critical components like; bearings, gears, and shafts of aluminium
wire rolling mill based on downtime, frequency of failures, loss of production in
terms of volume and cost. Understanding the failure modes, causes, effects and
consequences of failure pattern and problems faced in present maintenance practices.
(iv) Multi-criteria decision-making based different failure models for prioritizing
maintenance activities.
(v) Comparison of results of different MCDM models. Suggested improvement and
recommendations for future scope of the study.
(vi) It is assumed that presented failure model may not represent failures due to the first
point as adequate of design for such components are out of the scope of this study for
high failure rate.
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5. Original contribution by the thesis
In present research study, three distinct MCDM based models are proposed over traditional
FMECA to provide better alternative to maintenance practitioner for planning maintenance
activities accordingly. The research work will make following original contributions:
(i) The actual historical failure data like; run time, uptime, frequency of failures, average
repair or replace time are collected for the period of April-2013 to March-2014 for
identified aluminium wire rolling mill.
(ii) Reliability parameters like; mean time between failure (MTBF), mean time to repair
(MTTR), mean down time (MDT), hazard rate, availability are calculated based on
actual historical failure data to generate necessary inputs for further failure analysis.
(iii)The failure modes, causes, effects and consequences of failure pattern and problems
faced in present maintenance practices, as well as assignment of scores to failure
causes is based on actual shop-floor condition to find RPN and MCI through
traditional and MCDM approaches respectively.
(iv) The suggestions are discussed by comparing the results of three distinct MCDM
approaches. So, the work will present a strong case in developing the maintenance
plan to process industries of same or of a different kind.
6. Methodology of Research, Results / Comparisons
6.1 Overview, Failure Data Collection and Reliability Modeling
The aluminium wire rolling mill near Ahmedabad was selected for the research study. The
soft solid aluminium bar of 40 mm diameter is converted into 6 mm diameter wire through
series rolling process by fifteen stands with decreasing the diameter of wire by about 15-20 %
at each stand. In this study, the work is focused on fifteen stands, where reliability and
maintenance issues played a crucial role. Each stand has thirty-one components. The
historical failure data (Run time, Frequency of failures, Repair/Replace time) are collected
and recorded for all thirty-one components of all fifteen stands for the duration of April 2013
to March 2014 at Sampat Aluminium Pvt. Ltd., Ahmedabad, India. Then, the major reliability
parameters (MTBF, MTTR, MDT, Hazard rate, Availability etc.) are calculated from failure
data with the help of mathematical equations as follows (Balagurusamy 1984, Hoang Pham
2003, Khanna 2010, Mishra & Pathak 2012):
(1)
8
=
(2)
(3)
(4)
(
) (5)
(6)
(7)
Where; is Mean Time Between Failure, is frequency of failure, is hazard rate,
is Uptime, is Mean Down Time, is Down time, is Mean Time to Repair,
is Mean Time Between Maintenance, is Total time, is Operational Availability,
is Inherent Availability
6.2 Identification of Critical Components of Rolling Mill
The substantial reliability failure data are analyzed in a view to identifying the most critical
components based on their failure rate. The component criticality is decided based on the
downtime, frequency of failure, loss of production in terms of volume and cost for all
components of each stand. The critical components like; bearings, gears, and shafts are
identified which are common to major process industries. The failure modes, causes, effects
and consequences of failure pattern and problems faced in present maintenance practices are
derived based on actual shop floor condition.
6.3 Assignment of Score to Failure Causes
The score for each individual failure mode for every process input of critical components are
decided on; (i) historical failure data; which gives the comprehensive behavioral study of
failure pattern of critical components and (ii) questionnaires; to floor operators, managers and
maintenance personnel. The scores for each failure cause for every different criterion are
ranked on a scale of 1 – 10. The scale of 1 to 10 refers from least to most consideration of the
impact of criteria. The scores are assigned to three criteria; chances of failure (C), degree of
detectability (D) and severity of effect (S) for traditional FMECA. The scores are assigned to
9
six criteria; chances of failure (C), degree of detectability (D), degree of maintainability (M),
spare parts (SP), economic safety (ES) and economic cost (EC) for MCDM based FMECA
approaches. It is in crisp values for TOPSIS and PSI and in grey number range ] for
COPRAS-G. Table I and Table II displays the scores assigned as discussed above for
traditional and MCDM based FMECA respectively.
6.3 Failure Analysis Models
In FMECA, Risk Priority Number (RPN) is calculated by multiplying the scores of criteria;
chances of failure (C), degree of detectability (D) and severity of effect (S). The limitations
of FMECA are that it covers only limited criteria. Moreover, same importance is given to
each criterion without considering their relative importance. Also, a small variation of C or D
or S may change the value of RPN due to multiplication. To overcome the limitations of
FMECA, multi-criteria decision-making (MCDM) approaches are used with covering more
criteria. It compares alternatives relatively on weights which help decision-making process
effective.
TOPSIS is a multi-attribute decision-making system based on the measurement of the
Euclidean distance of each criterion from the ideal value which was first discussed in a crisp
version by Hwang and Yoon (1981). In this study, maintainability criticality index
( ) is calculated based on procedure as discussed by Sachdeva et al. (2009) for each
failure cause of critical components. COPRAS-G stands for Grey-Complex Proportional Risk
Assessment which works on grey number concept. The grey number is having upper and/or
lower limits whose value falls within an interval (Zavadskas et al. 2008, 2009 and Maity et al.
2012). This concept of grey was deduced from grey theory, which helps in dealing
uncertainty of real environment (Deng 1989, Chang et al. 1999 and Lin et al. 2008). In this
study, maintainability criticality index ( ) is calculated based on the procedure as
discussed by Zavadskas et al. (2008, 2009) and Maity et al. (2012). PSI stands for Preference
Selection Index. In PSI, maintainability criticality index ( ) is calculated through
method proposed by Maniya and Bhatt (2011).
Significance of COPRAS-G
During a brainstorming session, maintenance personnel score a criticality factor into different
criticality levels so it is challenging to do criticality analysis of failure modes accurately.
Hence this practical difficulty can be solving by expressing the scores of a criticality factor in
10
an interval (grey number) instead of certain and the exact value of TOPSIS. The main idea of
COPRAS-G method is to express the criteria values in intervals.
Significance of PSI
In preference selection index (PSI) method, preference values of each attribute are calculated
using the concept of statistics rather than assignment of weight attributes as in TOPSIS and
COPRAS-G. This method is very helpful in deciding the relative importance between
attributes when the situation of the conflict occurred.
TABLE I
MAINTENANCE PLANNING THROUGH TRADITONAL FMECA APPROACH
Particulars
Current Controls
Standard FMECA
Key
Process Input
Potential
Failure Mode
Potential
Causes
Potential Failure
Effects
C
D
S
Ris
k P
rio
rity
Nu
mb
er
Ran
k
What is the
Process
Input?
In what ways
can the
Process Input fail?
What causes the
Key Input to go
wrong?
What is the
impact on the
Key Output Variables once
it fails (customer or
internal
requirements)?
What are
the existing
controls and procedures
that prevent either the
Cause or the
Failure Mode?
Ho
w w
ell
can
you
det
ect
the
Cau
se o
r th
e F
ailu
re
Mod
e?
Ho
w o
ften
does
cau
se o
r F
M o
ccu
r?
Ho
w S
ever
e is
th
e ef
fect
to
th
e cu
stom
er?
Rolling
Mill Bearing
Failure
Bearing high
temperature
Improper
lubrication &
defective sealing
Bearing gets jammed/Bearing
housing jammed
Lubricating
the parts
when occurred
5 8 7 280 7
Bearing
corrosion
Higher speed
than specified
Increase in vibration &
noise
Proper
coolant 3 6 4 72 12
Bearing
fatigue
Design defects,
Bearing
dimension, not
as per
specification
Life reduction Bearing
replacement 9 7 10 630 1
Roller balls wear- out
Foreign matters/particles
Sudden rise in thrust
Regular
cleaning of
parts
8 6 7 336 4
Bearing
misalignment & improper
mounting
Sudden impact on the rolls
Shaft damage &
Impact damage
on other parts
Routine check up
8 5 8 320 5
Electrical damage
Loss of power Operation interrupted
Electrical
wiring
check up
2 1 7 14 14
11
Rolling
Mill Gearing
Failure
Gear teeth wear-out
Inadequate
lubrication - Dirt, viscosity
issues
Rough
operation & considerable
noise
Routine
check-up of
lubrication
5 2 5 50 13
Gear teeth
surface fatigue
(Pitting)
Improper
meshing, case depth & high
residual stresses
Gear life reduction
Preventive maintenance
8 5 8 320 6
Gear teeth scoring
Overheating at gear mesh
Interference &
backlash
phenomenon
Lubricating
when
needed
7 6 4 168 9
Gear teeth fracture
Excessive
overload &
cyclic stresses
Sudden
stoppage of
process plant
Break down maintenance
8 6 8 384 3
Gear teeth
surface
cold/plastic
flow
High contact stresses due to
rolling &
sliding action of mesh
Slippage &
power loss
Gear
replace
when
needed
5 3 5 75 11
Rolling Mill Shaft
(Primary
& Secondary)
Failure
Shaft fretting
Vibratory
dynamic load
from bearing
Leads to sudden failure
Break down maintenance
5 6 5 150 10
Shaft
misalignment
Uneven bearing
load
Vibration &
fatigue
Preventive
maintenance 7 7 8 392 2
Shaft fracture
(Fatigue)
Reverse & repeated cyclic
loading
Sudden stoppage of
process
Preventive
maintenance 7 4 8 224 8
7. Results, Discussion and Suggestions
In this study, the historical failure data of aluminium wire rolling mill are collected and
analyzed which help to understand behavioral failure pattern of components of rolling mill.
Moreover, major reliability parameters are calculated as a part of reliability analysis. The
results of criticality analysis show that bearings (70 %), gears (4 %) and shafts – primary &
secondary (4 %) are most critical components, which needs detailed FMECA to enhance the
working condition of the overall process industry. Remaining components cover of about 22
% of contribution for reliability loss on failure basis.
The results of traditional FMECA; failure modes with RPN more than 500 are considered
most critical and required to perform predictive maintenance, RPN from 250 to 500 are
considered critical and recommended preventive maintenance and less than 250 are
considered normal failures which are suggested corrective maintenance.
During failure pattern study, it is observed that almost 70 % downtime is due to bearing
failure and replacement practice is 100 %, so it is suggested to select standardize bearing with
appropriate specifications and mount them properly during every replacement to avoid
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bearing misalignment (C5) and minimizing reverse and repeated cyclic loading thus shaft
fatigue (C14) and gear tooth fracture (C10) can be avoided. It is suggested appropriate
condition monitoring to continuously record the condition of bearing damage and shaft
damage to prevent sudden breakdown and starting thrust on these components. Also, it is
suggested checking the condition of lubricants and replacing them whenever necessary rather
than routine clean up. Hence, sudden impact on the rolls (C5), design defects with bearing
dimension/specification (C3), foreign matters/particles (C4), excessive overload & cyclic
stresses (C10) and reverse & repeated cyclic loading (C14) can be covered under
recommendations.
Table III shows the comparison of results for traditional FMECA as well as MCDM based
FMECA (TOPSIS/COPRAS-G/PSI) approaches. It is suggested to modify the current control
practices as listed in Table I and Table II that failure causes (C5, C3, C4, C10, C14) with at
least large value of should be kept under predictive maintenance, failure cause (C13, C7,
C8, C1) with moderate value of should be kept under preventive maintenance and
failure causes (C2, C11, C12, C6, C9) with low should be kept under corrective
maintenance.
7. Achievements with respect to objectives
In present research study, three different MCDM based FMECA models are discussed for
optimizing existing maintenance practices to accomplish the objectives.
Objective (i):
This objective is achieved by visiting many process industries like; dairy plant (Sagar-Amul),
petrochemical plant (Nova), paper industry (Utility print pack), textile mill (Welspun), rolling
mill (Deora group) etc. to study reliability and maintenance issues faced by them. It is found
that average cost of maintenance or loss of reliability is about 8 to 25 % with existing
maintenance practices which are either breakdown or planned shutdown to various process
industries. It is derived the scope of improvement in existing maintenance practices.
Objective (ii):
This objective is achieved by identifying aluminium wire rolling mill situated near
Ahmedabad for further study. It is observed certain issues which affect reliability and needs
attention to enhancing maintenance plan during preliminary study. It is suggested certain
formats to gather the failure data and maintenance records. The failure data are collected and
analyzed for a period of a year which leads to reliability analysis and modeling.
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TABLE II MAINTENANCE PLANNING THROUGH TOPSIS, COPRAS-G & PSI FMECA APPROACHES
Particulars DECISION MATRIX
(TOPSIS/PSI ) DECISION MATRIX (COPRAS-G)
TOPSIS COPRAS-G PSI
No
tati
on
Key
Process
Input
Potential
Failure
Mode
Potential
Causes
Potential
Failure
Effects
Current
Controls C D M S
P
E
S
E
C C D M SP ES EC
What is
the
Process
Input?
In what ways
can the
Process Input
fail?
What causes
the Key
Input to go
wrong?
What is the
impact on
the Key
Output
Variables once it fails
(customer
or internal requirement
s)?
What are the
existing
controls and
procedures
that prevent either the
Cause or the
Failure Mode?
Chan
ce o
f fa
ilu
re
Det
ecti
on
pro
bab
ilit
y o
f
fail
ure
Mai
nta
inab
ilit
y c
rite
ria
Sp
are
par
ts c
rite
ria
Eco
no
mic
saf
ety
cri
teri
a
Eco
no
mic
co
st c
rite
ria
Chan
ce o
f fa
ilu
re
Det
ecti
on
pro
bab
ilit
y o
f fa
ilu
re
Mai
nta
inab
ilit
y c
rite
ria
Sp
are
par
ts c
rite
ria
Eco
no
mic
saf
ety
cri
teri
a
Eco
no
mic
co
st c
rite
ria
MC
I
Crit
icali
ty R
an
k
MC
I
Crit
icali
ty R
an
k
MC
I
Crit
icali
ty R
an
k
xij xij xij xij xij xij
Xi
j xij xij yij
Xi
j yij xij yij xij yij xij yij
Bearing
Failure
Bearing high
temperature
Improper lubrication &
defective
sealing
Bearing
gets jammed/Be
aring
housing jammed
Lubricating the parts
when
occurred
9 8 1 3 3 3 8 9 7 8 1 2 2 3 3 4 3 4 0.4265 9 0.1297 9 0.7094 3 C1
Bearing corrosion
Higher speed
than
specified
Increase in
vibration
& noise
Proper coolant
8 6 2 2 4 3 8 9 5 6 1 2 2 3 3 4 4 5 0.3640 10 0.1244 10 0.5486 11 C2
Bearing
fatigue
Design
defects, Bearing
dimension, not as per
specification
Life
reduction
Bearing
replacement
1
0 7 6 3
1
0 9 9
1
0 7 8 6 8 3 5 9
1
0 9
1
0 0.7986 2 0.2156 1 0.7842 2
C3
Roller balls
wear- out
Foreign
matters/parti
cles
Sudden rise
in thrust
Regular
cleaning of
parts
9 6 5 3 7 5 7 9 6 7 4 5 3 5 7 8 5 6 0.5794 3 0.1662 4 0.6622 6 C4
14
Bearing
misalignmen
t & improper mounting
Sudden impact on
the rolls
Shaft
damage & Impact
damage on
other parts
Routine
check up
1
0 5 6 5 9
1
0 8
1
0 5 6 6 7 5 7 9
1
0 9
1
0 0.8051 1 0.2079 2 0.9391 1 C5
Electrical
damage
Loss of
power
Operation
interrupted
Electrical wiring check
up
9 1 1 3 5 2 7 9 1 2 1 2 3 4 5 6 2 3 0.2499 14 0.1062 12 0.6444 7 C6
Rolling Mill
Gearing
Failure
Gear teeth
wear-out
Inadequate
lubrication -
Dirt, viscosity
issues
Rough operation &
considerabl
e noise
Routine
check-up of lubrication
7 3 5 3 7 4 7 8 2 3 5 6 3 4 7 8 4 5 0.4419 8 0.1401 8 0.5538 10 C7
Gear teeth
surface fatigue
(Pitting)
Improper meshing,
case depth & high residual
stresses
Gear life reduction
Preventive maintenance
8 5 5 3 5 5 8 9 4 5 5 6 3 4 4 5 5 6 0.4981 7 0.1444 7 0.5797 9 C8
Gear teeth
scoring
Overheating
at gear mesh
Interference
& backlash
phenomeno
n
Lubricating
when needed 5 4 2 3 3 3 4 5 3 4 2 3 2 3 2 3 3 4 0.2515 13 0.0863 14 0.5127 12
C9
Gear teeth fracture
Excessive
overload & cyclic
stresses
Sudden
stoppage of process
plant
Break down maintenance
9 2 6 4 7 7 9 10
2 4 6 7 3 4 7 8 7 8 0.5636 4 0.1700 3 0.6967 4 C10
Gear teeth surface
cold/plastic
flow
High contact stresses due
to rolling &
sliding
Slippage &
power loss
Gear replace
when needed 3 6 3 3 3 3 3 4 6 7 3 4 3 4 2 3 3 4 0.3460 12 0.1022 13 0.4518 14 C11
Rolling
Mill
Shaft (Primar
y &
Secondary)
Failure
Shaft fretting
Vibratory
dynamic
load from
bearing
Leads to
sudden failure
Break down
maintenance 5 5 4 3 3 3 5 6 4 5 3 5 3 4 3 4 3 4 0.3525 11 0.1096 11 0.4617 13 C12
Shaft
misalignment
Uneven
bearing load
Vibration
& fatigue
Preventive
maintenance 8 5 5 3 6 6 8 9 4 5 5 6 4 5 4 5 5 6 0.5505 5 0.1477 6 0.6131 8 C13
Shaft
fracture (Fatigue)
Reverse & repeated
cyclic
loading
Sudden
stoppage of process
Preventive
maintenance 9 2 6 4 6 7 9
1
0 2 3 6 7 3 4 5 6 6 7 0.5455 6 0.1526 5 0.6760 5 C14
15
TABLE III COMPARISON OF RESULTS FOR TRADITIONAL AS WELL AS MCDM (TOPSIS/COPRAS-G/PSI) FMECA
APPROACHES
Objective (iii):
In order to achieve this objective, the comprehensive reliability failure data are analyzed in a
view to identifying the most critical components based on the downtime, frequency of failure,
loss of production in terms of volume and cost for all components of each stand. The
identified critical components for further failure analysis are; bearings (bearing number
32308, 30310, 6213, 32222), gears (primary & secondary bevel gears with spigot and taper
end) and shafts (primary and secondary).
Objective (iv):
This objective is achieved by analyzing recorded historical failure data and actual shop-floor
conditions by methods of questionnaires to floor operators, managers and maintenance
personnel. The potential FMECA, as well as six criteria are derived and scores are assigned
on the scale of 1 to 10 from least to most consideration of the impact of criteria to failure
causes.
Objective (v):
This objective is achieved by applying traditional FMECA initially and results are derived
based on RPN. Then, maintainability criticality indices through three distinct multi-
criteria decision-making approaches; TOPSIS in crisp value, COPRAS-G in grey number
range and PSI without subjective weight consideration, are calculated for each failure cause
in a view to prioritize maintenance activities.
8. Conclusion
Looking to the actual failure analysis, lack of proper maintenance planning is the main reason
for the loss of reliability and poor productivity in process industries. In such condition, it is
necessary to optimize existing traditional maintenance practices based on real shop-floor
Method Result Analysis
1 Traditional FMECA
Most Critical (RPN: > 500) Critical (RPN: 250 < RPN <
500) Normal (RPN: < 250)
C3 C13, C10, C4, C5, C8, C1 C14, C9, C12, C11, C2, C7,
C6
MCDM Methods High MCI Moderate MCI Low MCI
1 TOPSIS Model C5, C3, C4, C10, C14 C13, C8, C7, C1, C12 C2, C11, C9, C6
2 COPRAS-G C3, C5, C10, C4, C14 C13, C8, C7, C1, C2 C12, C6, C11, C9
3 PSI C5, C3, C1, C10, C14 C4, C6, C13, C8, C7 C2, C9, C12, C11
16
conditions. In this research work, the failure pattern study of aluminium wire rolling mill
plant is demonstrated based on actual shop floor conditions through three distinct MCDM
failure models. Following conclusions are drawn from presented research work:
(i) Critical components are identified based on downtime, frequency of failures, loss of
production in volume and cost by analyzing actual historical failure data.
(ii) Research work is focused on potential failure causes of critical components like;
bearings, gears, and shafts of aluminium wire rolling mill which are common to major
process industries.
(iii)Scores for different failure causes are assigned on real shop-floor conditions
(iv) Maintenance planning is proposed in RPN for traditional FMECA and to overcome
the drawback of traditional FMECA, maintainability criticality indices are calculated
through three distinct multi-criteria decision-making approaches are; TOPSIS in crisp
value, COPRAS-G in grey number range and PSI where subjective weight
consideration not required for calculating .
(v) The results are helpful in prioritizing maintenance activities of process industry of
same or of different kinds in accordance with failure analysis.
(vi) The proposed study is challenging and interdisciplinary work; it will help to
understand about the working lives of components and associated failures, which lead
to re-engineer new technologies efficiently and to gain the operational advantage.
(vii) The study will be helpful in designing optimized maintenance plan to improve
plant efficiency as a whole.
9. Recommendations for Future Scope of Work
The research work presented can further be extended as under:
(i) The similar work can be extended to other process industries in a view to deciding
suitable maintenance strategy.
(ii) The results can be validated with similar or different kinds of process industries to
prove competency of MCDM based failure analysis models.
(iii)Similar work can be extended to other process industries such as; petrochemical plant,
textile mill etc. with other MCDM based approaches like; analytical hierarchy process
(AHP), qualitative flexible multi-criteria (QUALIFLEX) etc.
(iv) It is recommended to consider other criteria like; manpower skill, operating
conditions, environmental effect etc. to prioritize maintenance activities of process
industries.
17
(v) It is recommended the scope of the study to present failure model by considering
failures due to the first point with adequate of design for such components at high
failure rate.
(vi) Heuristic approaches can be further used to optimize maintenance activities by
considering reliability parameters as criteria of optimization.
(vii) Failure analysis approaches can be developed for various process industries
considering contemporaneous failures among various systems.
10. List of all publications arising from the thesis (Please Refer Table at the end)
11. References
Research Papers:
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for coal-fired thermal power plants using COPRAS-G,” International Journal of
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3. Barlow and Proshan (1975), Statistical Theory of Reliability and Life Testing, Holt,
Rinehart and Winston, New York.
4. Bevilacqua M., Braglia M., and Gabbrielli R., (2000), “Monte Carlo simulation
approach for a modified FMECA in a power plant,” Quality and Reliability
Engineering International, vol. 16, no. 4, 313–324.
5. Braglia M., (2000), “MAFMA: multi-attribute failure mode analysis,” International
Journal of Quality & Reliability Management, vol. 17, no. 9, pp. 1017–1033.
6. Braglia M., Frosolini M., and Montanari R., (2003), “Fuzzy TOPSIS approach for
failure mode, effects and criticality analysis,” Quality and Reliability Engineering
International, vol. 19, no. 5, pp. 425–443.
7. Braglia M., Frosolini M., and Montanari R., (2003), “Fuzzy criticality assessment
model for failure modes and effects analysis,” International Journal of Quality &
Reliability Management, vol. 20, no. 4, pp. 503–524.
8. Chanamool N., T. Naenna, (2016), “Fuzzy FMEA application to improve the
decision-making process in an emergency department,” Applied Soft Computing, vol.
43, pp. 441–453.
9. Chang C. L., Wei C. C., and Lee Y. H., “Failure mode and effects analysis using
fuzzy method and grey theory,” Kybernetes, vol. 28, no. 9, pp. 1072–1080, 1999.
10. Dekker (1996), Applications of maintenance optimization models: A review and
analysis, Reliability Engineering and System Safety 51 (3), 229–240
11. Deng J. L., (1989), “Introduction to grey system theory,” The Journal of Grey Theory,
vol. 1, no. 1, pp. 1–24.
18
12. Fragassa C, Pavlovic A., Massimo S., (2014), “Using a total quality strategy in a new
practical approach for improving the product reliability in automotive industry”,
International Journal for Quality Research, Vol: 8(3) 297-310, ISSN 1800-6450.
13. Gargama H., and Chaturvedi S. K., (2011), “Criticality assessment models for failure
mode effects and criticality analysis using fuzzy logic,” IEEE Transactions on
Reliability, vol. 60, no. 1, pp. 102–110.
14. Gilchrist W., (1993), “Modeling failure modes and effects analysis,” International
Journal of Quality & Reliability Management, vol. 10, pp. 16–23.
15. Hwang C. L., Yoon K., (1981), Multiple Attribute Decision Making: Methods and
Applications, vol. 186 of Lecture Notes in Economics and Mathematical Systems,
Springer, New York, NY, USA.
16. Jensen (1995), Stochastic models of reliability and maintenance: an overview, In
Ozekici, S. (Ed.), Reliability and maintenance of complex systems, NATO ASI series,
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Issues and Challenges in the Reliability and Maintenance of Complex Systems,
Kemer- Antalya, Turkey, June 12–22, pp. 3–36.
17. Lin Y. H., Lee P. C., and Ting H. I., (2008), “Dynamic multi-attribute decision-
making model with grey number evaluations,” Expert Systems with Applications, vol.
35, no. 4, pp. 1638–1644.
18. Maity S. R., Chatterjee P., and Chakraborty S., (2012) “Cutting tool material selection
using grey complex proportional assessment method,” Materials and Design, vol. 36,
pp. 372–378.
19. Maniya K. D., Bhatt M. G., (2011), “A selection of Material using a Novel type
Decision-making Method: Preference Selection Method”, Materials and Design, Vol.
31, pp. 1785-1789.
20. Mittal K., Tiwary P. C., Khanduja D, Kaushik P., (2016), “Application of Fuzzy
TOPSIS MADM approach in ranking & underlining the problems of plywood
industry in India”, Journal of Cogent Engineering, Vol.3, Issue 1, DOI:
10.1080/23311916.2016.1155839.
21. Mobin, M., Roshani, A., Saeedpoor, M., & Mozaffari, M. M. (2015), “Integrating
FAHP with COPRAS-G method for supplier selection (Case study: An Iranian
manufacturing company)”, Proceedings of the International Annual Conference of the
American Society for Engineering Management. (p. 1). American Society for
Engineering Management (ASEM).
22. Pham, Wang (1996), Imperfect maintenance, European Journal of Operational
Research, 94, 425–438.
23. Rastegari A., Archenti A., Mobin M., (2017), “Condition-based maintenance of
machine tools: Vibration monitoring of spindle units”, Reliability and Maintainability
Symposium (RAMS), pp. 1-8, IEEE, At: Florida (FL), USA.
19
24. Rathi R., Khanduja D., Sharma S. K., (2016), “A fuzzy-MADM based approach for
prioritising Six Sigma projects in the Indian auto sector”, International Journal of
Management Science and Engineering Management, Page 1-8,
http://dx.doi.org/10.1080/17509653.2016.1154486.
25. Sachdeva A., Kumar D., and Kumar P., (2009), “Multi-factor failure mode criticality
analysis using TOPSIS,” Journal of Industrial Engineering, International, vol. 5, no.
8, pp. 1–9.
26. Sahoo T., Sarkar P. K., and Sarkar A. K., (2014), “Maintenance optimization for
critical equipment in process industry based on FMECA method,” International
Journal of Engineering and Innovative Technology, vol. 3, no. 10, pp. 107–112.
27. Sikorska J, (2008), “Identifying Failure Modes Retrospectively Using RCM Data”.
28. Zavadskas E. K., Kaklauskas A., Turskis J., and Tamosaitiene J., (2008), “Selection
of the effective dwelling house walls by applying attributes values determined at
intervals,” Journal of Civil Engineering and Management, vol. 14, no. 2, pp. 85–93.
29. Zavadskas E. K., Kaklauskas A., Turskis J., and Tamosaitiene J., (2009), “Multi-
attribute decision-making model by applying grey numbers,” Informatica, vol. 20, no.
2, pp. 305–320.
30. Zhang F., (2015), “Failure modes and effects analysis based on fuzzy TOPSIS,” in
Proceedings of the IEEE International Conference on Grey System and Intelligent
Services (GSIS), pp. 588–593, Leicester, UK.
Books:
1. Balagurusamy E, “Reliability Engineering”, ISBN-13: 978-0-07-048339-2, Tata
Mcgraw Hill, New Delhi, 1984
2. Dhillon B. S., “Quality Control, Reliability, and Engineering Design”, ISBN: 0-8247-
7278-4, Marcel Dekker, New York, 1985
3. Hoang Pham, “Handbook of Reliability Engineering”, ISBN: 978-1-85233-453-6,
Springer, 2003
4. Khanna O.P., “Industrial engineering and management”, ISBN-13: 978-818992835,
Dhanpat rai & sons, 2010
5. Mishra, R C & Pathak K, “Maintenance Engineering and Management”, ISBN: 978-
81-203-4573-7, Prentice Hall of India Pvt Ltd., New Delhi, 2012
6. O’Connor P., “Practical Reliability Engineering, ISBN 13: 978-0-470-84462-5, John
Wiley, England, 2002
Websites Reports:
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REPORT 2014 – 2015 (September 2015), accessed via:
ieema.org/wpcontent/uploads/2015/09/IEEMA-Annual-Report_2014-15.pdf
2. “Indian Electrical Equipment Industry Mission Plan 2012-2022” Central electricity
authority, CEA website,
20
http://www.cea.nic.in/reports/monthly/installedcapacity/2016/installed_capacity-
03.pdf.
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http://www.gidb.org/Document/2014-12-31_112.pdf
4. Farley T., Miller D., “Maintaining Rolling Mill Performance Part I, II, III”, Innoval
Technology Ltd., Accessed via: http://www.innovaltec.com/aluminium-rolling-
models-blog/
Aluminium Wire Rolling Mill Process Flow
Melting of Aluminium ingots in furnace
Input
Semi solid cast bar through water
sprinking
Caster
Diameter reduction
through 15 stands in
series
Rolling Mill
Coiling of rod Output Dispatch
0.00
0.05
0.10
0.15
0.20
0.25
Haz
ard
Rat
e
Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14
Hazard Rate 0.14 0.16 0.15 0.16 0.18 0.19 0.16 0.15 0.15 0.18 0.21 0.23
Hazard Rate (Bath Tub) Curve
0.00
0.20
0.40
0.60
0.80
1.00
Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14
Ava
ilab
iliti
es
Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14
Operational Availability 0.78 0.76 0.78 0.77 0.74 0.74 0.75 0.79 0.76 0.73 0.71 0.68
Inherent Availability 0.92 0.91 0.92 0.92 0.91 0.90 0.91 0.93 0.92 0.90 0.89 0.87
Availability Curve
21
Shop-Floor Activities/Observations at Aluminium Wire Rolling Mill
0
100
200
300
400
500
600
700
800
Pri
mar
y Sh
aft
Pri
mar
y B
evel
…
Pin
fo
r En
try…
Ch
uck
Nu
t fo
r…
Top
Nu
t fo
r…
Spec
ial B
olt
fo
r…
Seco
nd
ary…
Bea
rin
g N
o.…
Bea
rin
g N
o.…
Oil
Seal
. 629
01
0…
Co
up
ler
Bo
lt
Loss
of
Pro
du
ctio
n V
olu
e/C
ost
Rolling Mill Components
Criticality Curve Based on Loss of Production Volume/Cost
C
1
C
2
C
3
C
4
C
5
C
6
C
7
C
8
C
9
C
10
C
11
C
12
C
13
C
14
TOPSIS FMECA 0.40.30.70.50.80.20.40.40.20.50.30.30.50.5
COPRAS FMECA 0.10.10.20.10.20.10.10.10.00.10.10.10.10.1
PSI FMECA 0.70.50.70.60.90.60.50.50.50.60.40.40.60.6
0.0000
0.1000
0.2000
0.3000
0.4000
0.5000
0.6000
0.7000
0.8000
0.9000
1.0000
MC
I
MCDM Based FMECA
TOPSIS…COPRAS…
22
List of Publications:
Sr.
No. Research Paper Title
Month-
Year of
Publicati
on
Journal/Conference
Details
ISSN/ISBN/
DOI Remark
1 Comparative Study of Traditional
Failure Mode Effect and Criticality
Analysis (FMECA) and TOPSIS
based FMECA for Bearings of
Aluminium Rolling Mill Plant – a
Case Study
January
2015
2nd
National Conference
on Emerging trends in
Engineering, Technology
& Management
(NCEETM), IU,
Ahmedabad
ISBN: 978-93-
80867-75-5
2 Multi criteria FMECA Based
Decision-Making for Aluminium
Wire Process Rolling Mill through
COPRAS-G
June
2016
Journal of Quality and
Reliability Engineering,
Volume 2016, Article ID
8421916, 8 pages
http://dx.doi.org
/10.1155/2016/
8421916
SCImago Rank:
0.22
Scopus
CiteScore
2016:0.53
H Index:4
J-Gate
3 Performance Reliability
Improvement by Optimizing
Maintenance Practices through
Failure Analysis in Process Industry
– A Comprehensive Literature
Review
December
2016
IOSR Journal of
Mechanical and Civil
Engineering (IOSR-
JMCE), Volume 13, Issue
6 Ver. I (Nov. - Dec.
2016), PP 66-73
e-ISSN: 2278-
1684, p-ISSN:
2320-334X
doi:
10.9790/1684-
1306016673
4 Traditional and Multi-factor
Decision Making based FMECA
through Preference Selection Index
Method for Continuous Process
Industry
January
2017
International Journal of
Darshan Institute on
Engineering Research &
Emerging Technologies
(IJDI-ERET), Vol. 5, No.
2, 2016, www.ijdieret.in
ISSN: (Print):
2320-7590
Impact
Factor:
4.483
5 Identifying Critical Components of
Identified Process Industry through
Shop-floor Failure Data
February
2017
International Conference
on Latest Concepts in
Science, Technology and
Management (ICLCSTM-
2017) at National Institute
of Technical Teachers
Training & Research
(NITTTR), MHRD, Govt
of India, Chandigarh,
ISBN: 978-81-
932712-4-7
International Journal of
Engineering Technology,
Management and Applied
Sciences, February 2017,
Volume 5, Issue 2
ISSN: 2349-
4476
6 TOPSIS and COPRAS-G based
Maintenance Optimization of
Aluminium Wire Rolling Mill
Components
March
2017
Journal of Basic and
Applied Research
International, Vol. 20(3):
pp.189-201, 2017
International Knowledge
Press
ISSN: 2395-
3438 (P),
ISSN: 2395-
3446 (O)
EBSCOhost
(USA)
7 Maintenance Planning through
FMECA based Multi-criteria
Decision-making PSI Approach for
Aluminium Wire Rolling Mill Plant
April
2017
IEEE 2nd
International
Conference for
Convergence of
Technologies (I2CT)
ISBN: 978-1-
5090-4307-1/17
Scopus/
Ei
Compendex
8 Traditional and TOPSIS based
Failure Mode Effect and Criticality
Analysis for Maintenance Planning
of Aluminium Wire Rolling Mill
Components
July 2017
GIT – Journal of
Engineering and
Technology (Tenth
Volume, 2017
ISSN: 2249 –
6157
9 Quality Enhancement in
Maintenance Planning through Non-
identical FMECA Approaches
June 2017
In Printing
International Journal for
Quality Research
ISSN: 1800-
6450
SCImago Rank:
0.234
Scopus
CiteScore
2016:0.78
H Index:7
10 FMECA based Maintenance
Planning through COPRAS-G and
PSI
Accepted
Aug’2017
In Printing
Journal of Quality in
Maintenance Engineering,
Emerald
ISSN: 1355-
2511
SCImago Rank:
0.340
Scopus:
CiteScore
2016: 1.16
H Index:41