Rolling Bearing Life Prediction Combined with CNN and TCN Neural Network
Aiming at the problems of insufficient feature extraction of rolling bearing vibration signals,over-reliance on manual feature extraction and low prediction accuracy.A prediction method based on CNN-TCN-Attention network model is proposed.In this method,the vibration signals of rolling bearings were selected as the input to enhance the features of the signals by EAVGH,and the deep features of the signals were extracted by using the convolutional neural network(CNN),and the TCN-Attention model was built to predict the remaining life of rolling bearings.Combining the Attention mechanism with the time convolutional network can effectively improve the prediction accuracy of the model.Through the bearing life experimental data verification,the CNN-TCN-ATTENTION prediction model can effectively extract the deep features in the vibration signals of rolling bearings,and has a high prediction accuracy.
Enhanced Morphologic Top Hat TransformationAttention MechanismTime Convolutional NetworkLife Prediction
NETL
NSTL
万方数据
