Monitoring of bearing fatigue life based on hidden Markov model

With the increase in the intelligence of the production process and the increase in reliability requirements, the monitoring of the bearing life status after the event has been unable to meet the needs of industrial production. Performance degradation assessment and life monitoring have attracted more attention as intelligent methods based on condition maintenance. Hidden Markov model is a statistical probability model based on time series, which is very suitable for modeling the performance degradation process of equipment. Therefore, this paper proposes a life monitoring algorithm based on hidden Markov model. First, the continuous wavelet transform is introduced to obtain the optimal value of the shape factor or the stretch factor. Secondly, a hidden Markov model of multi-channel information fusion is proposed. The algorithm significantly improves the effectiveness and robustness of life monitoring. The hidden Markov model explicitly expresses the state duration distribution, making the model more suitable for life monitoring.