Reliability Evaluation of Fuzzy Multi-state Systems Based on Stochastic Computation

Due to the lack, inaccuracy or fluctuation of data, conventional multi-state system (MSS) reliability analysis model suppose that the probability and performance level of each component are accurate. However, a few complex systems employ more than two states with fuzzy performance level and probability named fuzzy multi-state system (FMSS). Moreover, existing reliability analysis methods for FMSS such as fuzzy universal generating function (FUGF) and Monte Carlo (MC) have problems of high computational complexity and "state space explosion". To overcome these challenges, a general stochastic fuzzy multi-state (SFMS) model is proposed to analyze the reliability of FMSS in this paper. Each component performance level and probability represented by triangular fuzzy numbers (TFN) are simultaneously encoded in a multi-valued stochastic sequence. The mathematical calculation of FUGF is transformed into a logic gate of stochastic computation (SC), to process multi-valued stochastic sequences of components. A case study is presented to illustrate the accuracy and efficiency of proposed SFMS model compared to FUGF and MC approaches.

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