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www.etasr.com El Badawi et al.: Application of Failure Mode Effect and Criticality Analysis (FMECA) to a Computer … 
 

Application of Failure Mode Effect and Criticality 
Analysis (FMECA) to a Computer Integrated 

Manufacturing (CIM) Conveyor Belt 
 

Isam A.Q. Elbadawi 
Industrial Engineering Department 

University of Hail 
Hail, Saudi Arabia 

i.elbadawi@uoh.edu.sa 

Mohamed Arafat Ashmawy 
Mechanical Engineering Department 
University of Hail, Saudi Arabia and 

Suez University, Suez, Egypt 
arafat_696@yahoo.com 

Wan Ahmad Yusmawiza 
Industrial Engineering Department 

University of Hail 
Hail, Saudi Arabia 

yusmawiza@iium.edu.my 

Imran Ali Chaudhry 
Industrial Engineering Department 

University of Hail 
Hail, Saudi Arabia 

i.chaudhry@uoh.edu.sa 

Naim Ben Ali 
Industrial Engineering Department 

University of Hail 
Hail, Saudi Arabia 
naimgi2@yahoo.fr 

Ayyaz Ahmad 
Industrial Engineering Department 

University of Hail 
Hail, Saudi Arabia 

ay.ahmad@uoh.edu.sa 
 

 
Abstract—Fault finding and failure predicting techniques in 
manufacturing and production systems often involve forecasting 
failures, their effects, and occurrences. The majority of these 
techniques predict failures that may appear during the regular 
system production time. However, they do not estimate the 
failure modes and they require extensive source code 
instrumentation. In this study, we suggest an approach for 
predicting failure occurrences and modes during system 
production time intervals at the University of Hail (UoH). The 
aim of this project is to implement failure mode effect and 
criticality analysis (FMECA) on computer integrated 
manufacturing (CIM) conveyors to determine the effect of 
various failures on the CIM conveyor belt by ranking and 
prioritizing each failure according to its risk priority number 
(RPN). We incorporated the results of FMECA in the 
development of formal specifications of fail-safe CIM conveyor 
belt systems. The results show that the highest RPN values are 
for motor over current failure (450), conveyor chase of vibration 
(400), belt run off at the head pulley (200), accumulated dirt 
(180), and Bowed belt (150). The study concludes that performing 
FMECA is highly effective in improving CIM conveyor belt 
reliability and safety in the mechanical engineering workshop at 
UoH.  

Keywords-reliability; failure mode effect criticality analysis; 
maintenance 

I. INTRODUCTION 
Anticipating potential failure occurrences during conveyor 

productive time is important to achieve system pliability and to 
overcome unexpected and dangerous failure consequences. As 
machining tool systems become increasingly more 
complicated, the further development of system scale and 
functions, increases the probability of system failure. This 

paper starts with a brief historical overview of FMECA and the 
current international standards followed by a discussion of the 
core concept and implementation procedures of FMECA. 
Finally, the paper illustrates the application of FMECA on the 
CIM conveyor in the mechanical engineering workshop at 
UoH. A significant number of studies on the reliability and the 
related risks of manufacturing plants have been conducted. 
Consequently policies for risk management, maintenance 
policies, and suggestions for improving the production process 
have been developed. A number of these studies aimed at 
minimizing the downtime rate and improving the equipment’s 
availability and reliability. Furthermore, in order to improve the 
system efficiency with an optimized resource amount and to 
minimize the probability of system failure, several maintenance 
strategies were implemented, various approaches and 
maintenance planning models were developed, and many 
technical manufacturing specifications were listed. However, a 
comprehensive approach which tackles the potential causes and 
effects is yet to be developed [1]. 

II. FAILURE MODE EFFECT ANALYSIS (FMEA) 

A. FMEA 
Every machine or system used in the manufacturing 

industry might fail to work properly. The failure may be caused 
by machines, materials, measurements, manpower, methods, 
and/or environmental factors. Failure prediction methods have 
been used by leading production and manufacturing firms to 
control and minimize the impacts of failures on the cost of the 
product defect and the operational risk of manufacturing 
processes. A significant number of industrial firms and 
research organizations dedicated considerable efforts to analyze 
failure modes and their effects on manufacturing and 



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production systems. An important model was developed by the 
National Aeronautics and Space Administration (NASA) in 
which they used a number of reliability practices in several 
space programs such as Viking, Galileo, Apollo, and Voyager 
to analyze the failure mode behavior of their systems [2]. 
According to NASA, FMEA is a proactive quality tool for 
evaluating potential failure modes and their causes. FMEA 
helps in prioritizing the failure modes, recommending 
corrective measures for the avoidance of catastrophic failures 
and, therefore, improving the quality of the manufacturing and 
production processes. The objectives of FMEA include 
understanding the criticalities and dependencies of all types of 
systems as well as facilitating the evaluation of process 
manipulation options to achieve high level of system reliability. 

B. Failure Modes Effects and Criticality Analysis (FMECA) 
FMECA methodology is an advanced level of FMEA 

designed to assess the risk associated with all failure modes. 
The objective of FMECA is to design maintenance procedures 
required to eliminate points of failures as well as any 
catastrophic or critical consequence of such failures. The 
fundamental purpose is to initiate actions that reduce the 
likelihood of failure in the process. In 1965, the American 
aerospace manufacturer, Grumman, developed FMECA to 
identify the potential failures of the manipulation system of the 
flight vehicles powered by jet engine. The severity (S), 
occurrence (O) and detectability (D) of the failure effect can be 
analyzed and quantified to evaluate the risk associated with the 
potential problems identified through the analysis. 

 Severity. A value of 1 stands for an extremely low severity 
while a value of 10 stands for an extremely high severity as 
shown in Table I. 

 Occurrence is related to number of the preventive actions 
taken for the respective potential failure causes. The 
assessment of the probability for the occurrence of a 
potential failure cause is carried out while considering all 
listed preventive actions. A value of 10 is assigned, if it is 
likely that the potential failure cause will occur. A value of 
1 is assigned for a very improbable potential failure cause. 
Thus, the O assessment makes a statement concerning the 
quantity of defective components remaining in an entire 
batch of a certain product [3] (see Table I).  

 Detection is correlated to the actions taken to detect the 
respective potential failure causes. A value of 10 is assigned 
if no detection actions are mentioned whatsoever. A value 
of 1 is assigned, if the probability for the detection of the 
failure before the delivery to the customer is very high. 
Thus, the D assessment makes a statement concerning the 
quantity of undetected, defect components in an entire 
batch of a certain product as shown in Table I. 

In FMECA, risk is assessed with a value called risk priority 
number (RPN). RPN value is the quantitative measure in 
FMECA and is used to compare, analyze, and prioritize 
failures. These are important in order to suggest proper 
solutions and corrective maintenance procedures. RPN is 
calculated by multiplying Severity, Occurrence, and 
Detectability as shown in (1). 

RPN=S×O×D    (1) 

As shown in Table I, the highest RPN value is 1000 which 
indicates the most dangerous failure and the lowest RPN value 
is 1 which indicates an unnoticeable failure, a failure that 
would be solved before the customer notices. 

TABLE I.  RPN SCALE 

 
How Severe 
would the 
impact be? 

How likely is it to 
happen? 

How detectable 
is the problem?

What is the 
RPN? 

st
ra

te
gy

 

Reduce the 
severity of 

failure effects 

Reduce the failure 
rate 

Increase the 
detection rate of 
failure during the 

failure process 

 

 Severity Risk(S) 
Occurrence 

(O) 
Detectability 

(D) RPN=S×O×D

10 
Serious safety 

hazards without 
warning 

More 
than 1 in 

2 

Very 
High: 
failure 

evitable 

Absolute 
uncertainty: 

cannot detect the 
problem 

1000 Most dangerous

9 Hazard with warning 1 in 3 
Very 
high Very remote 729  

8 Very high 1 in 8 High Remote 512  
7 High 1 in 20 High Very low 343  
6 Moderate 1 in 80 Moderate Low 216  
5 Low 1 in 400 Moderate Moderate 125  

4 Very low 1 in 2000 Moderate Highly moderate 64  

3 Minor 1 in 15000 Low High 27  

2 Very minor 1 in 150000 Low Very high 8  

1 

None- No 
effect: 

customer might 
not notice it 

1 in 
1500000

Remote: 
Rare 

event, no 
data of 
such 

failure in 
the past 

Almost Certain: 
(Automation), 
current system 

certainly detects 
the failure 

1 

No one 
would 
notice: 
failure 

would be 
solved 

before the 
customer 
notices 

 

C. FMECA Standards 
There is a number of published guidelines and standards for 

the requirements and recommended reporting format of 
FMEAs and FMECAs [4]. Some of the main published 
standards for this type of analysis include SAE J1739and MIL-
STD-1629A. In addition, many industries and companies have 
developed their own procedures to meet the specific 
requirements of their products or processes [5]. 

 MIL-STD 1629 - Procedures for performing failure mode 
and effect analysis. 

 IEC 60812 - Procedures for failure mode and effect analysis 
(FMEA). 

 BS 5760-5 - Guide to failure modes, effects and criticality 
analysis (FMEA and FMECA). 



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 SAE ARP 5580 - Recommended failure modes and effects 
analysis (FMEA) practices for non-automobile applications 
[6]. 

 SAE J1739 - Potential Failure Mode and Effects Analysis 
in design (Design FMEA) and potential failure mode and 
effects analysis in manufacturing and assembly processes 
(Process FMEA) and effects analysis for machinery 
(Machinery FMEA) [7]. 

D. Benefits of FMECA 
FMECA allows: 

 High degree of complexity. 

 Uniform quantification of risk. 

 Results to be correlated directly with actual risks. 

 Easy modeling of the effect of various methods of 
mitigation/detection on risk. 

 Implementation of a well-documented record of 
improvements from corrective actions. 

 Acquiring information useful in developing test programs 
and in-line monitoring criteria. 

 Obtaining historical information useful in analyzing 
potential product failures during the manufacturing process. 

 Obtaining new ideas for improvements in similar designs or 
processes. 

E. Types of FMECA 
FMECA can be classified into three main types as shown in 

Figure 1. 

 

 
Fig. 1.  FMECA types 

III. STUDY OBJECTIVES 
This study employed FMECA techniques to minimize the 

failure mode of the CIM conveyor in the mechanical 
engineering workshop at the UoH. The study aims at providing 
CIM users with a general background about the techniques 
available for failure effects analysis and their maintainability 
analysis, reliability prediction and safety analysis. This paper 
proposes a risk based maintenance method, which relies on 
regular and automatic update of risk analyses of the equipment 
including the equipment failure history. The method provides 
up-to-date information about the equipment’s risks.  

IV. PREVIOUS WORK 
Authors in [8], presented risk in early design phase (RED) 

as a new method to introduce information about dysfunction 
during design phase of functionalities based on the following 
points:  

 The storage of breakdown events, (system breakdown 
database) 

 Matrix linking parts and functions, (system components 
function and relationship) 

 Translation of parts breakdown into risks of functional 
failure and database update. Risk pooling of system 
breakdown and system components failure.  

Later, they added an inspection module to improve 
maintenance operations schedule and to minimize failure risk 
by developing an optimal inspection strategy. They also 
pointed out the impact of the risk formalism on the assessment 
of occurrence, consequences and then the judgment of risk 
level. These results highlight the need for improving the quality 
of risk analyses.  

In [9], authors made a synthesis of 25 risk based 
maintenance (RBM) methods, presented their steps and 
described their main drawbacks. In particular they presented 
Khan and Haddara’s RBM process. They provided detailed 
description of each step as well as the factors affecting the 
quality of risk analysis evaluations. Three major factors and 
their related contributors were highlighted. Authors in [10] 
used the FMEA method to study the reliability of a wind 
turbine (WT) system, using a proprietary software reliability 
analysis tool. They compared the quantitative results of an 
FMEA and reliability field data from real wind turbine systems 
and their assemblies. Their results may be useful for future WT 
designs. Authors in [11] argue that a proper use of process 
Failure Modes and Effects Analysis (PFMEA) could be of a 
great importance for the automotive industry. Authors in [12] 
described the application of FMECA in Toshiba bulb 
factory in Monfiya, Egypt. They found that FMECA is an 
efficient technique for reducing the chances of catastrophic 
failures. The application of FMECA also helped to 
increase the reliability and availability of the machines in the 
factory. Authors in [13] devised an effective tool for solving 
the problems related to the quality of the manufacturing 
process through the application of FMECA. They identified 
and eliminated the problems they encountered during the 
manufacturing process of a cylinder head in an internal 



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combustion engine. However, the value of introducing FMECA 
to evaluate the reliability and to improve the maintenance 
process of the CIM conveyor belt in an academic setting is still 
under-researched. Thus, this study fills an important gap in this 
field.  

V. RESEARCH METHODOLOGY  

A. Conveyor Belt  
The conveyor system is part of the learning production 

system used in the workshops of the Mechanical Engineering 
department at the University of Hail. The conveyor consists of 
a feed belt and a rotary table. When students practice hands-on 
processing, the feed belt transports objects placed on its left 
end from the CIM robot to the right side of the assembly 
station. The belt has a bar code reader and photo-electric cells, 
which signal when an object arrives at its ends. Controlling the 
motion of conveyor belt is synchronized with a CIM robot and 
may be switched on and off: it has to be ON while waiting for a 
new object and has to be switched OFF when an object is at the 
end of the belt. The conveyor belt is used as a means to 
transport material from one station to another. A description of 
the conveyor belt is illustrated in Figure 2, and a brief of list of 
the specifications is provided in Table II. The conveyor belt 
either runs empty (while waiting for an object to be placed on 
it) or transports an object.  

TABLE II.  CONVEYOR BELT SPECIFICATIONS 

Parameter Value Unit 
Motor drive 2.5 HP 
Belt width 40 cm 

Assembly line length 6.4 m 
Belt speed 29 cm/s 

Assembly line height 87 cm 
Control pad height 130 cm 

Distance between each work station 1.5 m 
 

 
Fig. 2.  Conveyor belt diagram and dimensions  

B. Utilizing FMECA 
The methodology of this study utilizes FMECA according 

to the following sequence: FMECA scope, FMECA analysis, 
FMECA ranking, FMECA RPN calculations, FMECA 
verification, FMECA report . A detailed framework is 
illustrated in Figure 3. Data were collected and organized in a 
fishbone diagram as shown in Figure 4.The failure modes and 
their causes were identified for the conveyor belt. The three 
key indices (Severity, Occurrence and Detection) we reassessed 
and their analysis was carried out with the help of FMECA 
Worksheet. Finally, the necessary corrective actions were 
recommended. 

C. Ishakawa Fishbone Analysis 
The CIM conveyor belt was monitored for a total of 45 

hours (3 hours a week over the period of 15 weeks). 100 
failures were detected and categorized through Ishakawa 
fishbone analysis. The study found that the material type and 
property have the highest effect on the conveyor belt failures, 
while the method has the lowest. We investigated the list of 
conveyor belt failures in the Ishakawa fishbone and categorized 
them into a technical classification list as shown in Figure 5. 
We found that 45% of failures were caused by the belt of the 
conveyor and 2 percent by the robot alignments as shown in 
Figure 5.  

 

 
Fig. 3.  FMECA framework 

 

 
Fig. 4.  Conveyor belt failures Ishakawa fishbone 

 

 
Fig. 5.  Failures distribution. 



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D. Risk Priority Number (RPN) 
The failure modes and their causes were identified for each 

of the three key indices (Severity, Occurrence and Detection). 
We investigated 150 failures of the conveyor, and then 
categorized them into two groups: 

1) Belt failures 
The results summarized in FMECA Worksheet in Table III 

revealed that the RPN was the highest (RPN=200) for belt runs 
off at the head pulley, mainly owing to the degree of severity of 
the failure in disrupting the entire conveyor belt motion. Hence, 
extra attention should be given to the corrective measures for 
the belt runs off at the head pulley to eliminate the failure. The 
next priority should be given to the bowed belt (RPN=150), 
mainly because of its criticality affecting belt efficiency. For 
the excessive belt stretch (RPN=64) and belt slip (RPN=30) 
their effect is less than the previous two failures, but found to 
be the correlated to the head pulley alignments.  

2) Motor failures  
From Table IV it can be seen that the severity and 

occurrence of the motor over current and motor vibration are 
very high. The data revealed that both failures are critically 
affecting the CIM conveyor belt efficiency. Figure 6 shows that 
the motor over current RPN is 450 and it is the highest value, 
and the motor vibration RPN is 400. 

TABLE III.  RPN CONVEYOR BELT RESULTS 

Failure Mode S (1-10) 
O 

(1-10) 
D 

(1-10) RPN 

Belt slip 10 1 3 30 
Belt runs off at the head pulley 10 4 5 200 

Excessive belt stretch 8 4 2 64 
Bowed belt 6 5 5 150 

TABLE IV.  RPN MOTOR FAILURE RESULTS 

Motor Failures S O D RPN (1-10) (1-10) (1-10) 
Over-Current 9 10 5 450 

Low Resistance 8 3 1 24 
Over heating 6 1 9 54 

Dirt 9 4 5 180 
Moisture 6 5 1 30 
Vibration 10 10 4 400 

 

 
Fig. 6.  RPN motor failures distribution  

VI. CONCLUSION  
In this study the researchers designed a potential FMECA 

framework for the CIM conveyor belt as an analytical 
technique utilized primarily to ensure that the mechanisms of 
potential failure modes and their associated causes and effects 
have been considered and addressed. The researchers 
demonstrated that FMECA is sufficient to derive a reliable 
conveyor belt safely failure mode system by reducing the risk 
of failures. They proposed a FMECA framework to facilitate 
the ability to build and a plan for a preventive maintenance and 
corrective action. This increased the belt’s efficiency and 
reliability. The study developed a list of potential failure modes 
and ranked them according to their effect on the conveyor belt. 

ACKNOWLEDGMENT 
This research is supported by the deanship of academic 

research at University of Hail by a grant for project number 
(0150191). 

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