Equipment state recognition and fault prognostics method based on DD-HSMM model

Aiming at the problem of equipment operation state identification and fault prognosis, a Duration-Dependent Hidden Semi-Markov Model(DD-HSMM) was proposed. In this model, the historical operation information was merged into estimation process of Markov state transition probability matrix, thus the matrix had time variant characteristics. Furthermore, the parameter estimation method of Hidden Semi-Markov Model(HSMM) was studied based on improved forward-backward algorithm to make self-renewal by using historical operation information. The basic steps for predicting the Remaining Useful Life(RUL) of equipment was built by using fault rate method. Through a case of a rolling bearing's operation state to demonstrate the modeling process of proposed model, and the result showed that the proposed method was more effective than traditional HSMM model.