A robust system reliability analysis using partitioning and parallel processing of Markov chain. Issue 4 (30th September 2014)

• Record Type: Journal Article
• Authors: Lin, Po Ting; Chou, Yu-Cheng; Ting, Yung; Shyu, Shian-Shing; Chen, Chang-Kuo
• Editors: Tumer, Irem Y.; Lewis, Kemper
• Is Part Of: AI EDAM. Volume 28:Issue 4(2014)
• Page Start: 311
• Page End: 322
• Publication Date: 2014-09-30
• Subjects: Fault Tolerance, -- Markov Chain, -- M-Partitioning, -- Parallel Processing, -- Reliability Analysis; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060414000493
• Languages: English
• ISSNs: 0890-0604

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 N 2 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.