Journal of Quality and Reliability Engineering

Volume 2016, Article ID 8421916, 8 pages

http://dx.doi.org/10.1155/2016/8421916

## Multicriteria FMECA Based Decision-Making for Aluminium Wire Process Rolling Mill through COPRAS-G

^{1}Gujarat Technological University, Visat-Gandhinagar Highway, Chandkheda, Ahmedabad, Gujarat 382424, India^{2}Shantilal Shah Engineering College, Post: Vartej, Sidsar, Bhavnagar, Gujarat 364060, India

Received 4 March 2016; Revised 21 April 2016; Accepted 12 June 2016

Academic Editor: Kwai S. Chin

Copyright © 2016 Nilesh Pancholi and M. G. Bhatt. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper presents a multifactor decision-making approach based on “grey-complex proportional assessment (COPRAS-G) method” in a view to overcome the limitations of Failure Mode Effect and Criticality Analysis (FMECA). In this model, the scores against each failure mode are expressed in grey number instead of crisp values to evaluate the criticalities of the failure modes without uncertainty. The suggested study is carried out to identify the weights of major failure causes for bearings, gears, and shafts of aluminium wire rolling mill plant. The primary findings of the paper are that sudden impact on the rolls seems to be most critical failure cause and loss of power seems to be least critical failure cause. It is suggested to modify the current control practices with proper maintenance strategy based on achieved maintainability criticality index (MCI) for different failure causes. The outcome of study will be helpful in deriving optimized maintenance plan to maximize the performance of process industry.

#### 1. Introduction

The reliability and maintenance engineering is important to maintenance practitioners and reliability engineers to keep the system in a state of readiness. Moreover, it helps to identify the condition based faults, compare possible failure patterns, and maximize effectiveness in maintenance plan. There are many techniques available for planning maintenance activities of process industries. Traditional Failure Mode Effect and Criticality Analysis (FMECA) has proved to be prominence tool among maintenance personnel, where failure modes are ranked on risk priority number (RPN), which is the product of chances of failure (C), degree of detectability (D), and degree of severity (S) to prioritize the maintenance activities.

Traditional FMECA is a widely accepted methodology for prioritizing failure modes; however, it has some limitations. It does not cover the interdependency of different failure modes and their effects. It considers only limited criteria like C, D, and S and does not cover some important criteria like maintainability (M), spare parts availability (SP), economic safety (ES), economic cost (EC), and so forth which may also influence the failure modes. Moreover, same importance will be given to C, D, and S ignoring their relative importance and even small variation in the value of C or D or S may change the value of RPN significantly due to multiplication rule.

It has been observed that past researchers have undergone various modifications for improving FMECA to overcome these drawbacks for different processing units. Sahoo et al. [1] show that failure modes, effects, and critique analysis (FMECA) is an integral part of the technical design of maintenance and it represents a strong tool to evaluate and improve system reliability and therefore reduces costs associated with maintenance that is used in a wide range of industry. Some researchers [2–5] incorporated a new factor called operating conditions in the field of power plant. Anish et al. [5] presented a multifactor decision-making approach for prioritizing failure modes for paper industry as an alternative using TOPSIS. Braglia et al. [6, 7] presented fuzzy TOPSIS and Xu et al. [8] presented fussy assessment based FMEA for engine system. Gargama and Chaturvedi [9] introduced fuzzy RPN applying level sets where the three risk factors are expressed into fuzzy linguistic variables. Adhikary et al. [10] presented multicriteria FMECA for coal-fired thermal power station using COPRAS-G method. Zhang [11] presented integration of both subjective weights and objective weights to avoid failure modes from being underestimated or overestimated based on fuzzy TOPSIS to get the closeness coefficient for each failure mode. Chanamool and Naenna [12] highlight the importance of fuzzy FMEA for prioritization and assessment of failures that likely occur in the working process of an emergency department of hospitals. Liu et al. [13] presented a novel approach for FMEA based on combination weighting and fuzzy VIKOR method where integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach to deal with the uncertainty and vagueness from humans’ subjective perception and experience in risk evaluation process.

It has been observed that previous researchers did not consider COPRAS-G based multicriteria decision-making approach to process industries like aluminium wire rolling mill. In this paper COPRAS-G, a multicriteria decision-making tool, is applied to model FMECA in lieu of the traditional multiplication rule of the criticality factors.

#### 2. COPRAS-G Methodology

The concept of grey number was basically derived from grey theory, which deals with the decisions of uncertainty experienced in real-world environment [14–19]. The grey number is having upper and/or lower limits whose exact value is unknown but the interval within which the value falls is known [15–17]. Hwang and Yoon, 1981 [20], highlight importance of multicriteria decision-making (MCDM) where multiple and conflicting criteria are under consideration in different areas like personal, public, academic, or business contents.

The COPRAS-G method for criticality evaluation of failure modes is expressed through the following steps [15–17]:(1)Select the set of various criteria and failure modes and arrange them along the columns and the rows, respectively, in the decision matrix.(2)Construct the decision-making matrix which shows the criteria ranking in grey number intervals: where is the lower value and is the upper value of the interval. which represents the failure modes along the row and which represents the criteria along the column in decision matrix.(3)Normalize the decision matrix , as follows: Normalized decision matrix is as follows:(4)Calculate weight of each criterion based on Shannon’s entropy concept where initially we have to calculate entropy and from it weight for th criteria as follows:(5)Determine weighted normalized matrix as per the following equations: Weighted normalized decision matrix is as follows:(6)Calculate the weighted mean normalized sums for beneficial criteria whose larger values are preferable and for nonbeneficial criteria whose smaller values are preferable as follows: where , “” is the number of beneficial criteria, and () is the number of nonbeneficial criteria. All the beneficial criteria are placed in the decision-making matrix first and then the nonbeneficial criteria are placed.(7)Calculate the relative significance/weight MCI of each alternative as follows: where is the minimum value of all weighted mean normalized sums “” of nonbeneficial criteria. The criticality ranks (priorities) of alternatives are ranked according to the value of in increasing order; that is, larger value of is having higher priority than other alternatives. is the maximum value of relative significance/weight among all alternatives.(8)Calculate the degree of unity in percentage (%) contribution for th failure cause and assign rank based on value of MCI: where is the maximum value of relative significance/weight among all alternatives.

#### 3. Case Study

##### 3.1. Introduction

The proposed model is applied to the aluminium wire rolling mill processing plant situated in Gujarat, India. The detailed layout of process is given in Figure 1. The aluminium wire is produced through Properzi Process where solid aluminium bar of 40 mm is fed into stands to gradually reduce diameter to 6 mm rod through fifteen stands in series. At each stand diameter of rod decreases by about 15–20%. It is concluded that bearings, gears, and primary and secondary shafts are identified as most critical components based on historical comprehensive failure and repair data.