Bearing Fault Diagnosis Method Based on Information Fusion and Self-attention Convolutional Neural Network
Aiming at the problems of difficulty in bearing fault feature extraction,single input signal and low fault recognition rate,a bearing fault diagnosis method based on multi-head attention information fusion and self attention convolutional neural network(SA-CNN)was proposed.Firstly,the bearing fail-ure of metro traction motor was pre-made.The bearing test stand with variable working conditions was built and the experimental scheme was designed to collect the bearing vibration signal and sound emission signal.Next,the multi-head attention mechanism is employed to fuse the vibration fault signals and a-coustic emission signals of the bearings.Finally,the fused signals are put into a self-attentive mechanism convolutional neural network for fault diagnosis.The final results show that based on multi-head atten-tion information fusion and SA-CNN can effectively pay attention to bearing fault characteristic signals,and improve the accuracy of bearing fault diagnosis under varying working conditions.
国家科技期刊平台
NSTL
万方数据
