Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (5): 58-65.doi: 10.16180/j.cnki.issn1007-7820.2020.05.010

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Application of CHMM and AR Model in Evaluation and Prediction of Bearing Performance Degradation

LIU Yimin,LIU Tao,CHEN Qing   

  1. School of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650000,China
  • Received:2019-03-21 Online:2020-05-15 Published:2020-06-02
  • Supported by:
    National Natural Science Foundation of China(51675251);Applied Basic Research Key Project of Yunnan(201601PE00008)


The classic fault diagnosis technology can evaluate the operating status of the system in real time, but in practical applications, it is more desirable to predict the occurrence of the fault to guarantee personal and economic security. As a key component of mechanical equipment, the damage of the bearing may cause serious engineering accidents, so the bearing needs to be diagnosed. In this study, a continuous hidden Markov model was introduced, and the log likelihood was used as an evaluation index to evaluate performance degradation. The logarithm likelihood ratio based on the model output was combined with the autoregressive model, and then was used to predict the performance degradation of the bearing. The validity of the method was verified by comparing the two sets of full-life data. The results showed that the bearing performance degradation evaluation method based on continuous hidden Markov model was effective in evaluating the degradation of bearing performance, and the autoregressive model obtained more accurate results in life prediction.

Key words: continuous hidden Markov model, autoregressive, trend extrapolation, performance degradation assessment, performance degradation prediction, feature extraction

CLC Number: 

  • TP206 +.3