Fuzzy Reliability Analysis of Washing Unit in a Paper Plant Using Soft-Computing Based Hybridized Techniques
The present study deals with the fuzzy reliability analysis of washing unit in a paper plant utilizing available uncertain data which reflects their components’ failure and repair pattern. Paper computes different reliability parameters of the system in t
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Abstract The present study deals with the fuzzy reliability analysis of washing unit in a paper plant utilizing available uncertain data which reflects their components’ failure and repair pattern. Paper computes different reliability parameters of the system in the form of fuzzy membership functions. Two soft-computing based hybridized techniques namely Genetic Algorithms Based Lambda-Tau (GABLT) and Neural Network and Genetic Algorithms Based Lambda-Tau (NGABLT) along with traditional Fuzzy Lambda-Tau (FLT) technique are used to evaluate the fuzzy reliability parameters of the system. In FLT, ordinary fuzzy arithmetic is utilized while in GABLT and NGABLT ordinary arithmetic and nonlinear programming approach are used. The computed results, as obtained by these techniques, are compared. Crisp and defuzzified results are also computed. Based on results some important suggestions are given for future course of action in maintenance planning.
1 Introduction The important performance measure for repairable system are system reliability and availability. When system reliability is low, efforts are desired to improve it by reducing the failure rate or increasing the repair rate for each subsystem/component. To this effect the knowledge of system (or components) failure/repair Komal (&) Department of Mathematics, University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand 248007, India e-mail: [email protected] S. P. Sharma Department of Mathematics, Indian Institute of Technology Roorkee (IITR), Roorkee, Uttarakhand 247667, India e-mail: [email protected]
V. Snášel et al. (eds.), Soft Computing in Industrial Applications, Advances in Intelligent Systems and Computing 223, DOI: 10.1007/978-3-319-00930-8_10, Springer International Publishing Switzerland 2014
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behavior is customary in order to plan and adapt suitable maintenance strategies. Reliability analysts analyze the system reliability with the help of various qualitative and quantitative techniques such as reliability block diagram (RBD), fault tree analysis (FTA), event tree analysis (ETA), markov models (MM), Petri-nets (PN), failure mode and effect analysis (FMEA), Baysian approach etc [1–5]. These techniques generally require knowledge of precise numerical probabilities and functional component dependencies, information which are sometimes relatively difficult to obtain in any large-scale system. In view of these problems, selection of the appropriate method depends upon the complexity of the system and measures used to analyse system reliability. After selecting appropriate method or technique, system behavior is analysed by using collected or available historical data. But, data either collected or historical are often inaccurate, imprecise, vague and collected under different operating and environmental conditions. The causes may be age, adverse operating conditions and the vagaries of manufacturing processes which affect each part/unit of the system differently, and thus the issue is subject to
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