Maximum Likelihood Estimation for a Hidden Semi-Markov Model with Multivariate Observations

In this paper, a parameter estimation procedure for a condition-based maintenance model under partial observations is presented. The deterioration process of the partially observable system is modeled as a continuous-time homogeneous semi-Markov process. The system can be in a healthy or unhealthy operational state, or in a failure state, and the sojourn time in the operational state follows a phase-type distribution. Only the failure state is observable, whereas operational states are unobservable. Vector observations that are stochastically related to the system state are collected at equidistant sampling times. The objective is to determine maximum likelihood estimates of the model parameters using the ExpectationMaximization (EM) algorithm. We derive explicit formulae for both the pseudo likelihood function and the parameter updates in each iteration of the EM algorithm. A numerical example is developed to illustrate the efficiency of the estimation procedure. Copyright (C) 2012 John Wiley & Sons, Ltd.