A HMM and grey model based ERL forecasting method

This paper presents a hidden Markov model (HMM) based prognosis method for prediction of equipment health. 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 of the systems. The previous HMM based prognosis algorithm 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 machine, 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 calculate the expected residual life (ERL) by redefining the hazard rate. With the equipment health prognosis, we can predict the behavior of the equipment condition.