Axlebox Fault Diagnosis Method Based on Improved DANN and Attention Mechanism
To address the problem of poor performance of the existing deep learning network model on rolling bearing fault classifi-cation under variable operating conditions,taking the experimental data obtained from a rolling bearing bench as the research object,a rolling bearing fault diagnosis method was proposed based on the combination of migration learning and attention mechanism.A new net-work diagnosis model of WDAANN was obtained by combining domain adversarial neural network(DANN)and deep convolutional neural networks with wide first-layer kernel(WDCNN),and the network parameters were optimized by training the labeled data in the target domain;the proposed network was combined with the attentional mechanism to obtain a better classification ability,so as to realize the fault diagnosis of rolling bearings under variable operating conditions.Finally,the method was validated by comparing with traditional CNN,DANN and WDAANN models.The results show that the accuracy of the proposed method is improved,and the cross-domain diag-nostic ability of the model is improved;compared with WDCNN,CNN and WDAANN network,the performance of the proposed network is significantly improved,which verifies the superiority of the designed model.
deep learningtransfer learningvariable operating conditionattention mechanismclassification accuracy
万方数据
维普
