摘要
Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model( HMM). In this method,firstly,the delayed correlation-envelope method is used to extract features from vibration signals. Then,HMMs are trained respectively using data under normal condition,gear root crack condition and gear root breaking condition. Further,the trained HMMs are used in pattern recognition and model assessment. Finally,the results from standard HMM and the proposed method are compared, which shows that the proposed methodology is feasible and effective.
Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model（ HMM）. In this method,firstly,the delayed correlation-envelope method is used to extract features from vibration signals. Then,HMMs are trained respectively using data under normal condition,gear root crack condition and gear root breaking condition. Further,the trained HMMs are used in pattern recognition and model assessment. Finally,the results from standard HMM and the proposed method are compared, which shows that the proposed methodology is feasible and effective.
