Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 166, Issue -, Pages 3-15Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2016.11.019
Keywords
Multi-state system (MSS); Bayesian networks; Parameter inference; Multi-level observation sequences
Funding
- National Natural Science Foundation of China [71371042]
- Fundamental Research Funds for the Central Universities [ZYGX2015J082]
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Multi-state system reliability theory has received considerable attention in recent years, as it is able to characterize the multi-state nature and complicated deterioration process of systems in a finer fashion than that of binary-state system models. Parameter inference for multi-state system reliability models, which is a task that precedes reliability evaluation and optimization, is an interesting topic to be investigated. In this paper, a new parameter inference method, which aggregates observation sequences from multiple levels of a system, is developed. The proposed inference method generally consists of two stages: (1) compute the sequences of the posterior state probability distributions of units based on multi-level observation sequences by dynamic Bayesian network models and (2) estimate the unknown transition probabilities of units by converting the sequences of posterior state probability distributions into a least squares problem. Two illustrative examples, together with a set of comparative studies, are presented to demonstrate the effectiveness and efficiency of the proposed method.
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