Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis
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 forwardbackward 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. Through parameter estimation of the health-state duration probability distribution and the proposed backward recursive equations, one can predict the useful remaining life of the component. To determine the value of each sensor information, discriminant function analysis is employed to adjust the weight or importance assigned to a sensor. Therefore, sensor fusion becomes possible in this HSMM based framework. The validation of the proposed framework and methodology are carried out in real world applications: monitoring hydraulic pumps from Caterpillar Inc. The results show that the increase of correct diagnostic rate is indeed very promising. Furthermore, the equipment prognosis can be implemented in the same integrated framework.
hidden semi-markov model equipment health diagnosis prognosis sensor fusion