Due to imprecise information, it is always difficult for the system analyst to predict and enhance the performance of the system up to the desired degree of accuracy. Therefore, the main task is to reduce the uncertainty level for decision makers, so as to take a more sound decision in a reasonable time. For handling these issues, this paper addressed the various reliability parameters of the industrial system, which depicts the behavior of the system, by quantifying the uncertainties in the data in the form of fuzzy numbers. The corresponding membership functions of the system’s parameters are computed by formulating a nonlinear optimization model and solve it. The obtained results were compared with the existing as well as traditional methodology and results and found that they had less range of uncertainties during the analysis. A sensitivity as well as performance analysis has also been done for depicting the most critical component of the system. Finally, an approach has been illustrated through a case study of cattle feed plant, a repairable industrial system.
Uncertain data PSOBLT Fuzzy membership function Nonlinear optimization.
This is a preview of subscription content, log in to check access.
Cai KY (1996) System failure engineering and fuzzy methodology: an introductory overview. Fuzzy Sets Syst 83:113–133CrossRefGoogle Scholar
Chen K, Ma C, Zheng M, Gao F (2015) Optimization and mechanical accuracy reliability of a new type of forging manipulator. Chin J Mech Eng 28(2):236–248CrossRefGoogle Scholar
Cheng CH, Mon DL (1993) Fuzzy system reliability analysis by interval of confidence. Fuzzy Sets Syst 56(1):29–35CrossRefGoogle Scholar
Garg H (2013) Reliability analysis of repairable systems using Petri nets and Vague Lambda–Tau methodology. ISA Trans 52(1):6–18CrossRefGoogle Scholar
Garg H (2015) A Hybrid GA—GSA algorithm for optimizing the performance of an industrial system by utilizing uncertain data. Handbook of Research on Artificial Intelligence Techniques and Algorithms, IGI Global, pp 620–654. doi:10.4018/978-1-4666-7258-1.ch020
Garg H (2016) A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Appl Soft Comput 38:988–999CrossRefGoogle Scholar
Garg H (2016) A novel approach for analyzing the reliability of series-parallel system using credibility theory and different types of intuitionistic fuzzy numbers. J Braz Soc Mech Sci Eng 38(3):1021–1035CrossRefGoogle Scholar
Garg H, Rani M, Sharma SP (2014) An approach for analyzing the reliability of industrial systems using soft computing based technique. Expert Syst Appl 41(2):489–501CrossRefGoogle Scholar
Garg H, Rani M, Sharma SP, Vishwakarma Y (2014) Bi-objective optimization of the reliability-redundancy allocation problem for series-parallel system. J Manuf Syst 33(3):335–347CrossRefGoogle Scholar
Garg H, Sharma SP (2011) Behavior and system performance optimization for an industrial system by using particle swarm optimization. In: 2011 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2011), Guangzhou, China, pp 237–241Google Scholar
Garg H, Sharma SP (2012) Stochastic behavior analysis of industrial systems utilizing uncertain data. ISA Trans 51(6):752–762CrossRefGoogle Scholar
Garg H, Sharma SP, Rani M (2012) Stochastic behavior analysis of an industrial systems using PSOBLT technique. Int J Uncertain Fuzziness Knowl Based Syst 20(05):741–761CrossRefGoogle Scholar
Knezevic J, Odoom ER (2001) Reliability modeling of repairable systems using Petri nets and Fuzzy Lambda–Tau methodology. Reliab Eng Syst Saf 73(1):1–17CrossRefGoogle Scholar
Komal Sharma SP, Kumar D (2009) Stochastic behavior analysis of the press unit in a paper mill using GABLT technique. Int J Intell Comput Cybern 2(3):574–593MathSciNetCrossRefGoogle Scholar
Li C, Zhang Y, Xu M (2012) Reliability-based maintenance optimization under imperfect predictive maintenance. Chin J Mech Eng 25(1):160–165CrossRefGoogle Scholar