A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets

Authors:
Ying Peng;Ming Dong
Affiliations:
College of Economics & Management, Shanghai Jiao Tong University, 535 Fahua Zhen Road, Shanghai 200052, PR China;College of Economics & Management, Shanghai Jiao Tong University, 535 Fahua Zhen Road, Shanghai 200052, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This paper presents a hybrid prognosis approach of age-dependent hidden Markov model (HMM) and grey model (GM) for prediction of engineering asset health. Age-dependent HMM allows modeling the time duration of the hidden states and therefore is capable of prognosis. The estimated state duration probability distributions can be used to predict the remaining useful life (RUL) of the assets. The previous HMM based prognosis method assumed that the transition probabilities are only state-dependent. That is, the probability of making transition to a less healthy state does not increase with the age. In the proposed method, in order to characterize a deteriorating asset, an aging factor that discounts the probabilities of staying at current state while increasing the probabilities of transitions to less healthy states will be introduced. After the estimation of the aging factor, a grey model is used to compute the expected residual life (ERL) by redefining the hazard rate. With the asset health prognosis, the behavior of the asset condition can be predicted.