Parameter estimation in a condition-based maintenance model

Michael Jong Kim, Viliam Makis and Rui Jiang

Statistics & Probability Letters, 2010, vol. 80, issue 21-22, 1633-1639

Abstract: A parameter estimation problem for a condition-based maintenance model is considered. We model a failing system that can be in a healthy or unhealthy operational state, or in a failure state. System deterioration is assumed to follow a hidden, three-state continuous time Markov process. Vector autoregressive data are obtained through condition monitoring at discrete time points, which gives partial information about the unobservable system state. Two kinds of data histories are considered: histories that end with observable system failure and histories that end when the system is suspended from operation but has not failed. Maximum likelihood estimates of the model parameters are obtained using the EM algorithm and a closed form expression for the pseudo-likelihood function is derived. Numerical results are provided which illustrate the estimation procedure.

Keywords: Parameter; estimation; Partially; observable; failing; systems; Hidden; Markov; modeling; Vector; time; series; EM; algorithm (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (4)

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