Article ID: | iaor200927 |
Country: | Netherlands |
Volume: | 178 |
Issue: | 3 |
Start Page Number: | 858 |
End Page Number: | 878 |
Publication Date: | May 2007 |
Journal: | European Journal of Operational Research |
Authors: | He David, Dong Ming |
Keywords: | programming: markov decision, markov processes |
This paper presents an integrated platform for multi-sensor equipment diagnosis and prognosis. This integrated framework is based on hidden semi-Markov model (HSMM). Unlike a state in a standard hidden Markov model (HMM), a state in an HSMM generates a segment of observations, as opposed to a single observation in the HMM. Therefore, HSMM structure has a temporal component compared to HMM. In this framework, states of HSMMs are used to represent the health status of a component. The duration of a health state is modeled by an explicit Gaussian probability function. The model parameters (i.e., initial state distribution, state transition probability matrix, observation probability matrix, and health-state duration probability distribution) are estimated through a modified forward–backward training algorithm. The re-estimation formulae for model parameters are derived. The trained HSMMs can be used to diagnose the health status of a component.