Rolling Bearing Fault Diagnosis Method Based on Residual Network and Time-Frequency Domain Feature Fusion
Aiming at the limited feature extraction ability of vibration signals using a single time-frequency domain method,a fault diagnosis method of rolling bearing based on the fusion of residual network and time-frequency domain fusion was proposed.Vibration signals from the bearing were collected,and a set of 12 time domain characteristics of rolling bearing vibration signals were calculated,including dimensionless,quantitative feature statistical indexes and energy operator indexes.The time-domain signal was transformed into frequency-domain signal,from which four frequency-domain feature indexes were extracted.Discrete wavelet transform was used to extract the signal features,and 16 time-frequency domain features were obtained.The residual network was constructed and utilized to extract features from the original vibration signal.The extracted features in the time domain,frequency domain,and time-frequency do-main features were connected in the full connection layer to obtain 32 features,and then the fused features were combined with the time-domain features obtained from the residual network.Finally,the fused features were fed into a classification network to obtain fault diag-nosis results.According to the experimental verification on the dataset of a key laboratory and the real operation dataset from enterprises,the proposed method has better performance than other classical classification models.
rolling bearingfault diagnosisresidual networkfeature extraction
万方数据
维普
