Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5019385 | Reliability Engineering & System Safety | 2017 | 34 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Mechanical Engineering
Authors
Tao Jiang, Yu Liu,