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A robust system reliability analysis using partitioning and parallel processing of Markov chain

  • Po Ting Lin (a1), Yu-Cheng Chou (a2), Yung Ting (a3), Shian-Shing Shyu (a4) and Chang-Kuo Chen (a4)...
Abstract
Abstract

This paper presents a robust reliability analysis method for systems of multimodular redundant (MMR) controllers using the method of partitioning and parallel processing of a Markov chain (PPMC). A Markov chain is formulated to represent the N distinct states of the MMR controllers. Such a Markov chain has N2 directed edges, and each edge corresponds to a transition probability between a pair of start and end states. Because N can be easily increased substantially, the system reliability analysis may require large computational resources, such as the central processing unit usage and memory occupation. By the PPMC, a Markov chain's transition probability matrix can be partitioned and reordered, such that the system reliability can be evaluated through only the diagonal submatrices of the transition probability matrix. In addition, calculations regarding the submatrices are independent of each other and thus can be conducted in parallel to assure the efficiency. The simulation results show that, compared with the sequential method applied to an intact Markov chain, the proposed PPMC can improve the performance and produce allowable accuracy for the reliability analysis on large-scale systems of MMR controllers.

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Corresponding author
Reprint requests to: Yu-Cheng Chou, Institute of Undersea Technology, National Sun Yat-sen University, 70 Lienhai Road, Kaohsiung City 80424, Taiwan. E-mail: ycchou@mail.nsysu.edu.tw
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AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
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