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Fuzzy reliability analysis of repairable industrial systems using soft-computing based hybridized techniques |
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| Affiliation: | 1. Department of Mathematics, Birla Campus, H.N.B. Garhwal University (A Central University), Srinagar (Garhwal) 246174, Uttarakhand, India;2. Department of Mathematics, Indian Institute of Technology Roorkee (IITR), Roorkee 247667, Uttarakhand, India;1. Department of Cardiothoracic Surgery, Washington University in St. Louis School of Medicine, St. Louis, Missouri;2. Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, Missouri;3. Department of Public Health, Temple University, Philadelphia, Pennsylvania;4. Section of Cardiac Surgery, Yale University School of Medicine, New Haven, Connecticut;1. Department of Computer Science, Rani Anna Government College for Women, Tirunelveli, Tamil Nadu, India;2. Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India;1. School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Viet Nam;2. Faculty of Information Technology, Le Quy Don Technical University, Hanoi, Viet Nam;3. Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Sakai, Japan |
| Abstract: | The purpose of the present study is to analyze the fuzzy reliability of a repairable industrial system utilizing historical vague, imprecise and uncertain data which reflects its components’ failure and repair pattern. Soft-computing based two different hybridized techniques named as Genetic Algorithms Based Lambda–Tau (GABLT) and Neural Network and Genetic Algorithms Based Lambda–Tau (NGABLT) along with a traditional Fuzzy Lambda–Tau (FLT) technique are used to evaluate some important reliability indices of the system in the form of fuzzy membership functions. As a case study, all the three techniques are applied to analyse the fuzzy reliability of the washing system in a paper mill and results are compared. Sensitivity analysis has also been performed to analyze the effect of variation of different reliability parameters on system performance. The analysis can help maintenance personnel to understand and plan suitable maintenance strategy to improve the overall performance of the system. Based on results some important suggestions are given for future course of action in maintenance planning. |
| Keywords: | FLT technique GABLT technique NGABLT technique Nonlinear programming Genetic algorithm Artificial neural networks (ANN) |
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