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Uncertainty analysis of an industrial system using Intuitionistic Fuzzy Set Theory

Author

Listed:
  • Yashi Vishwakarma

    (Indian Institute of Technology Roorkee)

  • S. P. Sharma

    (Indian Institute of Technology Roorkee)

Abstract

In this paper, the uncertainty in the operational behavior of an industrial system has been analysed using Intuitionistic Fuzzy Set (IFS) Theory. The performance of systems depends on various reliability parameters such as system failure rate, mean time to repair (MTTR), mean time between failures (MTBF), expected number of failures (ENOF), system reliability and availability. Therefore it is necessary for plant personnel to have relevant information about these parameters. As primary data related to failure and repair time are extracted from literature/experiments/expert opinions it may contain some uncertainty and impreciseness. To deal with the impreciseness and explore the information about the system parameters, IFS theory has been incorporated here. In this study, the behavior analysis and reliability measures of TAB Manufacturing Plant are discussed by Vague Lambda-Tau (VLT) method which is based on IFS theory and lambda-tau technique. The objective of this study is to analyse and find more information about reliability indices and to show the importance of Vague Lambda-Tau method over Fuzzy Lambda-Tau method. For this purpose, all the system parameter-values are obtained by Fuzzy Lambda-Tau method and are compared with Vague Lambda-Tau method values. The uncertainty analysis, done here will help the decision analyst to achieve better system maintenance strategy for failure-free operation of the system.

Suggested Citation

  • Yashi Vishwakarma & S. P. Sharma, 2016. "Uncertainty analysis of an industrial system using Intuitionistic Fuzzy Set Theory," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 73-83, March.
    • Handle: RePEc:spr:ijsaem:v:7:y:2016:i:1:d:10.1007_s13198-015-0384-0
    DOI: 10.1007/s13198-015-0384-0
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    References listed on IDEAS

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    1. S.P. Sharma & Harish Garg, 2011. "Behavioural analysis of urea decomposition system in a fertiliser plant," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 8(3), pages 271-297.
    2. Sharma, Rajiv Kumar & Kumar, Sunand, 2008. "Performance modeling in critical engineering systems using RAM analysis," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 913-919.
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    Cited by:

    1. Shilpa Srivastava & Millie Pant & Namrata Agrawal, 2018. "Psychology of adolescents: a fuzzy logic analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 66-81, February.
    2. Mohammad Nadjafi & Mohammad Ali Farsi & Hossein Jabbari, 2017. "Reliability analysis of multi-state emergency detection system using simulation approach based on fuzzy failure rate," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(3), pages 532-541, September.
    3. Nitin Sachdeva & Ompal Singh & P. K. Kapur & Diego Galar, 2016. "Multi-criteria intuitionistic fuzzy group decision analysis with TOPSIS method for selecting appropriate cloud solution to manage big data projects," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(3), pages 316-324, September.

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