期刊
NAVAL RESEARCH LOGISTICS
卷 62, 期 3, 页码 190-205出版社
WILEY
DOI: 10.1002/nav.21622
关键词
condition-based maintenance; reliability; mean residual life; hidden semi-Markov model; expectation-maximization algorithm
资金
- Natural Sciences and Engineering Research Council of Canada [RGPIN 121384-11]
We consider a partially observable degrading system subject to condition monitoring and random failure. The system's condition is categorized into one of three states: a healthy state, a warning state, and a failure state. Only the failure state is observable. While the system is operational, vector data that is stochastically related to the system state is obtained through condition monitoring at regular sampling epochs. The state process evolution follows a hidden semi-Markov model (HSMM) and Erlang distribution is used for modeling the system's sojourn time in each of its operational states. The Expectation-maximization (EM) algorithm is applied to estimate the state and observation parameters of the HSMM. Explicit formulas for several important quantities for the system residual life estimation such as the conditional reliability function and the mean residual life are derived in terms of the posterior probability that the system is in the warning state. Numerical examples are presented to demonstrate the applicability of the estimation procedure and failure prediction method. A comparison results with hidden Markov modeling are provided to illustrate the effectiveness of the proposed model. (c) 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 190-205, 2015
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