Paper Format MMQC14

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2ndNational Conference on Latest Develeopments in Materials,

Manufacturing and Quality Controlfrom 13-14th February, 2014.

Abstract- In this paper, a multi factor decision makingapproach is presented for prioritizing failures causes for thedigester of pulping system of a paper mill as an alternative totraditional approach of failure mode and effect analysis

(FMEA). The approach is based on the technique for orderpreference by similarity to ideal solution (TOPSIS). The

priority ranking is formulated on the basis of six parameters(failure occurrence, non detection, maintainability, spare

parts, economic safety and economic cost). The Shannon'sentropy concept is used for assigning objective weights tomaintenance parameters.

Keywords - Maintenance, TOPSIS, FMECA, andMCDM, Risk priority number.

I. INTRODUCTION

Deciding on the best maintenance policy is not aneasy matter as the maintenance program must combine

technical requirements with the management strategy. A

good maintenance program must define maintenancestrategies for different facilities. The failure mode of every

component must be studied in order to assess the best

maintenance solution, in accordance with its failure

pattern, impact and cost on the whole system. Thisinformation helps the maintenance personnel to decide the

best suited maintenance action and to assign the different

priorities to various plant components and machines. Themanagement of large number of tangible and intangible

attributes that must be taken into account represents thecomplexity of the problem.

Several techniques have been discussed in theliterature for planning maintenance activities of industrial

plants. The most commonly used technique to evaluate the

maintenance significance of the items/ failure modes andcategorise these in several groups of risk is based on usingfailure mode effect and criticality analysis FMCEA. This

methodology has been proposed in different possiblevariants, in terms of relevant criteria considered and/or

risk priority number formulation. Using this approach, theselection of a maintenance policy is performed through

the analysis of obtained priority risk number.This technique was first proposed by NASA in 1963

for their obvious reliability requirements. Since then, ithas been extensively used as a powerful technique for

system safety and reliability analysis of products and

processes in a wide range of industries [1], [2]. The main

objective of FMEA is to discover and prioritise thepotential failure modes by computing respective RPN,which is a product of failure occurrence, severity and

probability of non detection of a fault. Though RPN

evaluation with FMEA is probably the most popular

technique for reliability and failure mode analysis, severalproblems are associated with its practical implementation,

which have been addressed by many authors[3],[4],[5],[6],[7]. The most important problems discussed

were regarding the- FMCEA does not consider the interdependence among

the various failure modes and effects;- It considers only three kinds of attributes whereas other

important aspects like economic aspects, production

quantities, and safety aspects etc. are not taken intoconsideration

- It is assumed that the three indexes are equallyimportant and identifying situations with the same

priority number characterized by different index levels.- the assumption that the scales of three severity (S),

occurrence (O) and detection (D) indexes have the

same metric and that the same design level corresponds

to the same values on different index scales;- different sets of the three factors can produce exactly

the same value of RPN, but the hidden implication maybe totally different

- The method of multiplication adopted for calculatingthe risk priority number is questionable.

Considering the importance and complexity of themaintenance design problem it is observed that further

efforts are needed for the development of effectivemethods which will incorporate numerous evaluation

criteria and help the maintenance staff in evaluating the

impact of intangible factors in the maintenance decisionmaking and identify the best maintenance policy

accordingly.

In this paper, a new technique based on modifiedFMEA alongwith TOPSIS is proposed to determine the

risk priority number and to overcome the limitations of theconventional RPN, as cited above. This technique permits

to take into consideration the several possible aspectsconcerning the maintenance selection problem (failure

chances, detectability, costs and safety aspect etc.) Themethod is based on a technique for order preference bysimilarity to ideal solution (TOPSIS). TOPSIS is a multi-

attribute decision making methodology based on themeasurement of the Euclidean distance of an alternative

from an ideal goal [8]. The procedure for TOPSIS

methodology is presented in the subsequent section.

Maintenance criticality analysis using TOPSIS

Anish Sachdeva1, Pradeep Kumar

2, Dinesh Kumar

2

1Department of Industrial and Production Engineering, National Institute of Technology, Jalandhar, India

([email protected])2Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, India


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TABLE 1

SCORE FOR CHANCE OF FAILURE

Occurrence MTBF Score

Almost never >3 years 1

Rare 2-3 years 2

Very few 1-2 years 3

few 3/4-1 year 4

Medium 6 - 9 months 5

Moderately high 4 - 6 months 6

High 2 - 4 months 7

Very high 1 - 2 months 8

Extremely high < 30 days 9

TABLE 2SCORE FOR NON DETECTION OF FAILURES

Likelihood ofNon-detection (%)

Criteria for non detection offailures

Score

< 10 Extremely low 1

10-20 Very low 2

21-30 Low 3

31-40 fair 4

41-50 medium 5

51-60 moderately high 6

61-70 High 7

71-80 Very high 8

> 80 Extremely high 9

TABLE 3

SCORE FOR MAINTAINABILITY

Criteria Maintainability Score

Mt > 0.8 Almost certain 1

0.7 < Mt0.8 Very high 20.6 < Mt0.7 High 3

0.5 < Mt0.6 Moderate ly high 4

0.4 < Mt0.5 Medium 5

0.3 < Mt0.4 Low 6

0.2 < Mt0.3 Very low 7

0.1 < Mt0.2 Slight 8

Mt< 0.1 Extremely low 9

TABLE 4SCORING CRITERIA FOR SPARE PARTS

CriticalityAvailability

Easy Difficult Scarce

Desirable 1 4 7

Essential 2 5 8

Vital 3 6 9

TABLE 5

SCORES FOR ECONOMIC SAFETY LOSS

Status of the equipment/ sub system Score

With no moving parts 2

With one moving part/ critical category 3

With two moving parts/ critical category 5

With three moving parts/ critical category 7With more than three moving parts/ critical category 9

TABLE 6

SCORES FOR ECONOMIC COST

Criteria for Economic Cost Score

Extremely low 1

Very low 2

Low 3

Fair 4

Medium 5

Moderat ely high 6

High 7

Very high 8

Extremely high 9

TABLE 7

SCORES FOR FAILURE CAUSES OF DIGESTERMajor Components and

Potential cause of failureO D M SP ES EC

Wire mesh

Abrasion of mesh [D1]

Corrosion [D2]

Foreign material [D3]

Vacuum pumps

Lack of lubrication in moving parts.

[D4]

Bearing failure [D5]

Inclusion of solid particles[D6]

Seal failure [D7]

Let down relief valve

Mechanical failure [D8]

Blockage. [D9]

3

5

8

4

5

5

8

5

4

5

5

8

5

5

7

5

8

7

3

6

4

6

8

5

6

5

4

4

4

2

4

4

3

3

8

7

3

3

3

6

6

6

3

3

3

3

4

4

5

7

7

5

7

6

Figure labels should be legible, approximately 8 to 10

point type.

Fig. 1 Mapping nonlinear data to a higher dimensional feature

space


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2ndNational Conference on Latest Develeopments in Materials,

Manufacturing and Quality Controlfrom 13-14th February, 2014.

V.CONCLUSIONS

This paper presents a new modified FMECA approach todeal with the problems encountered while defining the

best mix of maintenance policies. An objective weightedfunction based multi criteria failure mode analysis

technique using TOPSIS is proposed to find more accurate

and reliable priority risk numbers for performing the

criticality analysis. This enables to obtain a ranking offailure modes/components by incorporating several types

of information related to performance, safety and society.

In particular, the analysis of prioritization of failure causesprovides a framework to decide upon the type of

maintenance strategies for different failure modes. Ifreliable quantitative judgments are available for some

criteria, then it can also be easily included in the analysis.So the use of the proposed approach forms a basis for the

continuous process of reliability design and maintenance

strategy decisions.

REFERENCES

[1] P. O'Connor, Practical Reliability Engineering, Singapore: JohnWiley & Sons, 2003.

[2] S. H. Teng, and S. Y. Ho, Failure mode and effects analysis: anintegrated approach for product design and process control,

International Journal of Quality & Reliability Management, vol.

13, no. 5, pp. 8-26, 1996.

[3] B. G. Dale and C. Cooper, Total Quality and Human Resources:An Executive Guide, Oxford :Blackwell Publishers, 1992.

[4] D. Straker, A Tool book for Quality Importance and ProblemSolving, London: Prentice-Hall International Limited, 1995.

[5] J. B. Bowles and C. E. Pelaez, Fuzzy logic prioritization offailures in a system failure mode, effects and criticality analysis,

Reliability Engineering and System Safety, vol. 50, pp. 203213,

1995.

[6] M. Braglia, MAFMA: multi-attribute failure mode analysis,International Journal of Quality & Reliability Management, vol.

17, pp. 1017-1033, 2000.[7] N. R. Shankar and B. S. Prabhu, Modified approach for

prioritizat ion of failures in a system failure mode and effects

analysis, Internat ional Journal of Quality & ReliabilityManagement, vol. 18, no. 3, pp. 324-335, 2001.

[8] M. Braglia, M. Frosolini, and R. Montanari, Fuzzy TOPSISapproach for failure mode, effects and criticality analysis,Quality and Reliability Engineering International, vol. 19, pp.

425443, 2003.

[9] C. L. Hwang, and K. Yoon, Multi Attribute Decision MakingMethods and Applications, New York: Springer-Verlag, 1981.

[10] C. Parkan, and M. Wu, Decision-making and performancemeasurement models with applications to robot selection,

Computers & Industrial Engineering, vol. 36, pp. 503-23,1999.[11] D. Jee, and K. Kang, A method for optimal material selection

aided with decision making theory, Materials and Design, vol.

21, pp. 199-206, 2000.[12] H. Deng, C. Yeh and R. J. Willis, Inter-company comparison

using modified TOPSIS with objective weights, Computers &

Operations Research, vol. 27, pp. 963-973, 2000.

APPPENDIX A

THE DISTANCES OF FAILURE CAUSES FROM IDEAL SOLUTION

Failure Causes

O D M SP ES EC

+

i1d -

i1d +

i2d -

i2d +

i3d -

i3d +

i4d -

i4d +

i5d -

i5d +

i6d -

i6d

D1 0.1064 0.0000 0.0545 0.0000 0.1064 0.0000 0.0571 0.0571 0.0476 0.0000 0.0833 0.0000

D2 0.0638 0.0426 0.0545 0.0000 0.0426 0.0638 0.0571 0.0571 0.0476 0.0000 0.0625 0.0208

D3 0.0000 0.1064 0.0000 0.0545 0.0851 0.0213 0.1143 0.0000 0.0476 0.0000 0.0625 0.0208

D4 0.0851 0.0213 0.0545 0.0000 0.0426 0.0638 0.0571 0.0571 0.0000 0.0476 0.0417 0.0417

D5 0.0638 0.0426 0.0545 0.0000 0.0000 0.1064 0.0571 0.0571 0.0000 0.0476 0.0000 0.0833

D6 0.0638 0.0426 0.0182 0.0364 0.0638 0.0426 0.0857 0.0286 0.0000 0.0476 0.0000 0.0833D7 0.0000 0.1064 0.0545 0.0000 0.0426 0.0638 0.0857 0.0286 0.0476 0.0000 0.0417 0.0417

D8 0.0638 0.0426 0.0000 0.0545 0.0638 0.0426 0.0000 0.1143 0.0476 0.0000 0.0000 0.0833

D9 0.0851 0.0213 0.0182 0.0364 0.0851 0.0213 0.0286 0.0857 0.0476 0.0000 0.0208 0.0625


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