Journal of Quality and Reliability Engineering

Journal of Quality and Reliability Engineering / 2016 / Article

Research Article | Open Access

Volume 2016 |Article ID 8421916 | 8 pages |

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

Academic Editor: Kwai S. Chin
Received04 Mar 2016
Revised21 Apr 2016
Accepted12 Jun 2016
Published14 Jul 2016


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 [25] 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 [1419]. 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 [1517]. 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 [1517]:(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.

To decide the score for each individual failure mode for every process input of critical components, the following methods are used:(i)Historical failure data which gives comprehensive behavioral study of failure pattern of critical components.(ii)Questionnaires to floor operators, managers, and maintenance personnel.The score for chances of failure, detectability, maintainability, spare parts, economic safety, and economic cost is ranked as per Tables 1, 2, 3, 4, 5, and 6, respectively.


Almost neverMore than three years1
Very rareOnce every 2-3 years2
RareOnce every 1-2 years3
Very lowOnce every 11-12 months4
LowOnce every 9-10 months5
MediumOnce every 7-8 months6
Moderate highOnce every 5-6 months7
HighOnce every 3-4 months8
Very highOnce every 1-2 months9
Extremely highLess than 1 month10

Chances of detectionLikelihood of nondetection (%)Score

Best 10 to 202
Better21 to 303
Good 31 to 404
Easy41 to 505
Occasional 51 to 606
Late61 to 707
Difficult 71 to 808
Very difficult81 to 909
Impossible91 to 10010

Maintainability scopeCriteria for measureScore

Extremely high<101
Very high10 to 202
High21 to 303
Moderate high31 to 404
Medium41 to 505
Low51 to 606
Very low61 to 707
Rare71 to 808
Very rare81 to 909
Almost nil91 to 10010

Criteria for availability and requirementScore

Easily available & desirable1
Easily available & essential2
Easily available & very essential3
Hard to procure but desirable4
Hard to procure but essential5
Hard to procure but very essential6
Scarce and desirable7
Scarce and essential8
Scarce and very essential9
Impossible and urgent10

Criteria for economic safetyScore

Extremely low1
Very low2
Moderately high7
Very high9
Extremely high10

Criteria for economic costScore

Extremely low1
Very low2
Moderately high7
Very high9
Extremely high10

3.2. Importance of Use of COPRAS-G

During 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 solved by expressing the scores of a criticality factor in an interval (grey number) instead of certain and exact value (white number). In this problem, COPRAS-G method, a multifactor decision-making tool, is used by expressing criticality factors with grey numbers in lieu of the traditional multiplication rule. The main idea of COPRAS-G method is to express the criteria values in intervals, which comes from real situation of decision-making.

3.3. Failure Mode Effect and Criticality Analysis with Assignment of Score in Grey Number Range

The potential failure modes, their causes, and failure effect of bearings, gears, and primary and secondary shafts are generated through the root cause analysis method. The scores for chances of failure (C), degree of detectability (D), degree of maintainability (M), spare parts (SP), economic safety (ES), and economic cost (EC) for various failure causes are ranked on scale of 1–10 as per concept of grey number range in [] based on Tables 16, where is the lower value and is the upper value of the interval as reflected in Table 7. The scales of 1 to 10 signify from least to most consideration of impact of criteria and are assigned on basis of questionnaires and brainstorming session to floor operators, shop floor managers, and maintenance personnel for various individual failure causes (C1 to C14).

Particulars⁢Decision matrixWeighted mean normalized sum⁢Relative significance/weight⁢% contribution⁢Criticality rank⁢
Key process inputPotential failure modePotential causesPotential failure effectsCurrent controls⁢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 requirements)?What are the existing controls and procedures that prevent either the cause or the failure mode?⁢Chance ⁢of ⁢failure⁢Detection ⁢probability of ⁢failure⁢Maintainability ⁢criteria⁢Spare ⁢parts ⁢criteria⁢Economic ⁢safety ⁢criteria⁢Economic ⁢cost ⁢criteria

Bearing failure⁢Bearing high temperatureImproper lubrication & defective sealingBearing gets jammed/bearing housing jammedLubricating the parts when occurred8978122334340.12970.1297609
Bearing corrosionHigher speed than specifiedIncrease in vibration & noiseProper coolant8956122334450.12440.12445810
Bearing fatigueDesign defects & bearing dimension not as per specificationLife reductionBearing replacement9107868359109100.21560.21561001
Roller balls wear-outForeign matters/particlesSudden rise in thrustRegular cleaning of parts7967453578560.16620.1662774
Bearing misalignment & improper mountingSudden impact on the rollsShaft damage & impact damage on other partsRoutine check-up8105667579109100.20790.2079962
Electrical damageLoss of powerOperation interruptedElectrical wiring check-up7912123456230.10620.10624912

Rolling mill gearing failure⁢Gear teeth wear-outInadequate lubrication-dirt, viscosity issuesRough operation & considerable noiseRoutine check-up of lubrication7823563478450.14010.1401658
Gear teeth surface fatigue (pitting)Improper meshing, case depth & high residual stressesGear life reductionPreventive maintenance8945563445560.14440.1444677
Gear teeth scoringOverheating at gear meshInterference & backlash phenomenonLubricating when needed4534232323340.08630.08634014
Gear teeth fractureExcessive overload & cyclic stressesSudden stoppage of process plantBreakdown maintenance91024673478780.17000.1700793
Gear teeth surface cold/plastic flowHigh contact stresses due to rolling & sliding action of meshSlippage & power loseGear replacement when needed3467343423340.10220.10224713

Rolling mill shaft (primary & secondary) failureShaft frettingVibratory dynamic load from bearingLeads to sudden failureBreakdown maintenance5645353434340.10960.10965111
Shaft misalignmentUneven bearing loadVibration & fatiguePreventive maintenance8945564545560.14770.1477686
Shaft fracture (fatigue)Reverse & repeated cyclic loadingSudden stoppage of processPreventive maintenance91023673456670.15260.1526715


4. Results and Discussion

Table 8 shows the relative significance/weight of each alternative MCI and the degree of unity in percentage (%) contribution () for th failure cause which is derived as per COPRAS-G methodology discussed in Section 2.

Failure cause versus criteriaWeighted mean normalized sumRelative weight% contributionCriticality rank
Failure causeNotationMCIRank

Bearing high temperatureC10.12970.1297609
Bearing corrosionC20.12440.12445810
Bearing fatigue C30.21560.21561001
Roller balls wear-outC40.16620.1662774
Bearing misalignment & improper mountingC50.20790.2079962
Electrical damageC60.10620.10624912

Gear teeth wear-outC70.14010.1401658
Gear teeth surface fatigue (pitting)C80.14440.1444677
Gear teeth scoringC90.08630.08634014
Gear teeth fractureC100.17000.1700793
Gear teeth surface cold/plastic flowC110.10220.10224713

Shaft frettingC120.10960.10965111
Shaft misalignmentC130.14770.1477686
Shaft fracture (fatigue)C140.15260.1526715

It has been observed from Table 8 that design defects and bearing dimension not as per specification (C3) seems to be most critical failure cause and overheating at gear mesh (C9) seems to be least critical failure cause. It is suggested to modify the current control practices as listed in Table 1 that failure causes (C3, C5, C10, C4, and C14) with large value of MCI should be kept under predictive maintenance, failure causes (C13, C8, C7, C1, and C2) with moderate value of MCI should be kept under preventive maintenance, and failure causes (C12, C6, C11, and C9) with low MCI should be kept under corrective maintenance.

Moreover, it has been observed that almost 70% down time is due to bearing failure and replacement practice is 100%, so it is recommended to select standardized bearing with appropriate specifications and mount them properly during every replacement to avoid bearing misalignment (C5) and minimizing reverse and repeated cyclic loading; thus shaft fatigue (C14) and gear tooth fracture (C10) can be avoided. Appropriate condition monitoring is suggested to continuously record the condition of bearing damage and shaft damage to prevent sudden breakdown and starting thrust on these components. Also, the condition of lubricants should be checked and replaced 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 and cyclic stresses (C10), and reverse and repeated cyclic loading (C14) can be covered under recommendations. Failure causes with moderate and low MCI are controlled under preventive and corrective maintenance practices. Comparison matrix for deciding maintenance strategy is shown is Table 9.

Sr. numberFailure causeSuggested maintenance strategyImpact of MCI & ()

1C3, C5, C10, C4, C14Predictive (condition based) maintenanceHigh MCI & ()
2C13, C8, C7, C1, C2Preventive maintenanceModerate MCI & ()
3C12, C6, C11, C9Corrective maintenanceLow MCI & ()

5. Conclusion and Scope

This paper highlights multicriteria decision-making approach based on COPRAS-G to overcome the limitations of FMECA. The case study presented in this paper shows how to deal with the problems encountered in aluminium wire rolling mill processing plant with mix of maintenance practices. It is concluded that the study will be helpful in deriving optimized maintenance plan to improve plant efficiency as a whole. The similar work can be extended for process industries of same or of different kinds in a view to decide suitable maintenance strategies in coordination with failure analysis.

Competing Interests

The authors declare that they have no competing interests.


The authors are thankful to Sampat Aluminium Pvt. Ltd., Ahmedabad, Gujarat, India, and its maintenance personnel, managers, and shop floor executives for giving them kind and valuable support in fulfillment of requirements directly or indirectly during this study.


  1. T. Sahoo, P. K. Sarkar, and A. K. Sarkar, “Maintenance optimization for critical equipments in process industry based on FMECA method,” International Journal of Engineering and Innovative Technology, vol. 3, no. 10, pp. 107–112, 2014. View at: Google Scholar
  2. W. Gilchrist, “Modeling failure modes and effects analysis,” International Journal of Quality & Reliability Management, vol. 10, pp. 16–23, 1993. View at: Google Scholar
  3. M. Bevilacqua, M. Braglia, and R. Gabbrielli, “Monte Carlo simulation approach for a modified FMECA in a power plant,” Quality and Reliability Engineering International, vol. 16, no. 4, pp. 313–324, 2000. View at: Google Scholar
  4. M. Braglia, “MAFMA: multi-attribute failure mode analysis,” International Journal of Quality & Reliability Management, vol. 17, no. 9, pp. 1017–1033, 2000. View at: Publisher Site | Google Scholar
  5. S. Anish, D. Kumar, and P. Kumar, “Multi-factor failure mode criticality analysis using TOPSIS,” Journal of Industrial Engineering, International, vol. 5, no. 8, pp. 1–9, 2009. View at: Google Scholar
  6. M. Braglia, M. Frosolini, and R. Montanari, “Fuzzy TOPSIS approach for failure mode, effects and criticality analysis,” Quality and Reliability Engineering International, vol. 19, no. 5, pp. 425–443, 2003. View at: Publisher Site | Google Scholar
  7. M. Braglia, M. Frosolini, and R. Montanari, “Fuzzy criticality assessment model for failure modes and effects analysis,” International Journal of Quality & Reliability Management, vol. 20, no. 4, pp. 503–524, 2003. View at: Publisher Site | Google Scholar
  8. K. Xu, L. C. Tang, M. Xie, S. L. Ho, and M. L. Zhu, “Fuzzy assessment of FMEA for engine systems,” Reliability Engineering & System Safety, vol. 75, no. 1, pp. 17–29, 2002. View at: Publisher Site | Google Scholar
  9. H. Gargama and S. K. Chaturvedi, “Criticality assessment models for failure mode effects and criticality analysis using fuzzy logic,” IEEE Transactions on Reliability, vol. 60, no. 1, pp. 102–110, 2011. View at: Publisher Site | Google Scholar
  10. D. D. Adhikary, G. K. Bose, D. Bose, and S. Mitra, “Multi criteria FMECA for coal-fired thermal power plants using COPRAS-G,” International Journal of Quality & Reliability Management, vol. 31, no. 5, pp. 601–614, 2014. View at: Publisher Site | Google Scholar
  11. F. Zhang, “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, 2015. View at: Google Scholar
  12. N. Chanamool and T. Naenna, “Fuzzy FMEA application to improve decision-making process in an emergency department,” Applied Soft Computing, vol. 43, pp. 441–453, 2016. View at: Publisher Site | Google Scholar
  13. H.-C. Liu, J.-X. You, X.-Y. You, and M.-M. Shan, “A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method,” Applied Soft Computing, vol. 28, pp. 579–588, 2015. View at: Publisher Site | Google Scholar
  14. J. L. Deng, “Introduction to grey system theory,” The Journal of Grey Theory, vol. 1, no. 1, pp. 1–24, 1989. View at: Google Scholar
  15. E. K. Zavadskas, A. Kaklauskas, Z. Turskis, and J. Tamošaitiene, “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, 2008. View at: Publisher Site | Google Scholar
  16. E. K. Zavadskas, A. Kaklauskas, Z. Turskis, and J. Tamosaitiene, “Multi attribute decision making model by applying grey numbers,” Informatica, vol. 20, no. 2, pp. 305–320, 2009. View at: Google Scholar
  17. S. R. Maity, P. Chatterjee, and S. Chakraborty, “Cutting tool material selection using grey complex proportional assessment method,” Materials and Design, vol. 36, pp. 372–378, 2012. View at: Publisher Site | Google Scholar
  18. C.-L. Chang, C.-C. Wei, and Y.-H. Lee, “Failure mode and effects analysis using fuzzy method and grey theory,” Kybernetes, vol. 28, no. 9, pp. 1072–1080, 1999. View at: Publisher Site | Google Scholar
  19. Y.-H. Lin, P.-C. Lee, and H.-I. Ting, “Dynamic multi-attribute decision making model with grey number evaluations,” Expert Systems with Applications, vol. 35, no. 4, pp. 1638–1644, 2008. View at: Publisher Site | Google Scholar
  20. C. L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications, vol. 186 of Lecture Notes in Economics and Mathematical Systems, Springer, New York, NY, USA, 1981. View at: MathSciNet

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.

More related articles

1107 Views | 214 Downloads | 5 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.