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.
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Komal, Sharma, S.P. (2014). Fuzzy Reliability Analysis of Washing Unit in a Paper Plant Using Soft-Computing Based Hybridized Techniques. In: Snášel, V., Krömer, P., Köppen, M., Schaefer, G. (eds) Soft Computing in Industrial Applications. Advances in Intelligent Systems and Computing, vol 223. Springer, Cham. https://doi.org/10.1007/978-3-319-00930-8_10
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DOI: https://doi.org/10.1007/978-3-319-00930-8_10
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