Journal
IEEE TRANSACTIONS ON RELIABILITY
Volume 64, Issue 1, Pages 394-409Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2014.2366292
Keywords
Bayesian estimation; continuous inspection data; discontinuous inspection data; multi-state component; multi-state system; reliability assessment
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Funding
- National Natural Science Foundation of China [71101017]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20110185120014]
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This paper develops a Bayesian framework to assess the reliability and performance of multi-state systems (MSSs). An MSS consists of multiple multi-state components of which the degradation follows a Markov process. Due to the lack of sufficient data, and only vague knowledge from experts, the transition intensities of multi-state components between any pair of states and the state probabilities cannot be precisely estimated. The proposed Bayesian method can merge prior knowledge from experts' judgments with continuous and discontinuous inspection data to obtain posterior distributions of transition intensities. A simulation method embedded with the universal generating function (UGF) is developed to estimate the posterior state probabilities, the reliability, and the performance of the entire MSS. Two numerical experiments are presented to demonstrate the effectiveness of the proposed method.
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