Research on Bearing Fault Diagnosis Method Based on DRSN Fusion Trans-former Encoder
Aiming at the problems of low diagnosis accuracy and weak generalization performance of bearing faults in complex working conditions,a bearing fault diagnosis method based on deep residual shrinkage network(DRSN)fusion Transformer encoder is proposed.First,DRSN is used to automatically remove the noise information in the vibration signal through the soft threshold module,and the attention mechanism is used to enhance the extracted features.Then,the Transformer encoder is used to further solve the long-term dependence problem in the vibration signal,and finally the softmax function is used to realize multi-fault mode recognition.The proposed model is tested on the Case Western Reserve University(CWRU)dataset through different noise levels.The experimental results show that the method achieves classification of bearing faults,with higher accuracy in strong noise environments and fast training time.
NETL
NSTL
万方数据
维普
