Fault Diagnosis for Rolling Bearings Based on Convolutional Attention-based Feature Transfer Learning
Aiming at the problem that the accuracy of rolling bearing fault diagnosis is low due to the large difference in the distri-bution of sample data between the source domain and the target domain under variable working conditions,a new transfer learning method called convolutional attention-based feature transfer learning(CAFTL)is proposed,which applied in the fault diagnosis of rolling bearings under variable working conditions.In the proposed CAFTL model,the source and target domain samples are input to the convolutional neural network after the multi-head self-attentive computation and normalization to obtain the corresponding source and target domain features;Then,the two domain features are projected into the same common feature space by the domain adaptive transfer learning network;And then,the classifier constructed from the source domain labeled samples is used for the classification;Finally,the stochastic gradient descent(SGD)method is used to train and update the parameters of the model,after the optimal pa-rameters of the model are obtained,the optimized model is used for the fault diagnosis of rolling bearing samples to be tested.A fault diagnosis example for rolling bearings verifies the effectiveness of the proposed method.
万方数据
维普
