Optimal Bayesian fault prediction scheme for a partially observable system subject to random failure
AbstractA new method for predicting failures of a partially observable system is presented. System deterioration is modeled as a hidden, 3-state continuous time homogeneous Markov process. States 0 and 1, which are not observable, represent good and warning conditions, respectively. Only the failure state 2 is assumed to be observable. The system is subject to condition monitoring at equidistant, discrete time epochs. The vector observation process is stochastically related to the system state. The objective is to develop a method for optimally predicting impending system failures. Model parameters are estimated using EM algorithm and a cost-optimal Bayesian fault prediction scheme is proposed. The method is illustrated using real data obtained from spectrometric analysis of oil samples collected at regular time epochs from transmission units of heavy hauler trucks used in mining industry. A comparison with other methods is given, which illustrates effectiveness of our approach.
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 214 (2011)
Issue (Month): 2 (October)
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Web page: http://www.elsevier.com/locate/eor
Maintenance Stochastic optimization Failure prediction Hidden Markov modeling Multivariate Bayesian control;
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- Makis, Viliam, 2009. "Multivariate Bayesian process control for a finite production run," European Journal of Operational Research, Elsevier, vol. 194(3), pages 795-806, May.
- Makis, Viliam & Wu, Jianmou & Gao, Yan, 2006. "An application of DPCA to oil data for CBM modeling," European Journal of Operational Research, Elsevier, vol. 174(1), pages 112-123, October.
- Serge Provost & Edmund Rudiuk, 1996. "The exact distribution of indefinite quadratic forms in noncentral normal vectors," Annals of the Institute of Statistical Mathematics, Springer, vol. 48(2), pages 381-394, June.
- Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.
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