Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

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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: ,
Keywords: programming: markov decision, markov processes
Abstract:

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.

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