Multiscale Pooled Convolutional Domain Adaptation Network for Intelligent Diagnosis of Rolling Bearing Under Variable Conditions

Deep learning-based fault diagnosis methods usually require samples to meet the conditions of independent and identical distribution. In actual industrial occasions, the data distribution of mechanical equipment under variable operating conditions is different, which results in the degradation of diagnostic performance. To overcome the above shortcomings, a fault diagnosis method based on multiscale pooled convolutional domain adaptation network is proposed. In the method, a novel parallel multiscale pooled module is designed to replace the traditional convolution module in 1-D-CNN. The four parallel branches in the structure contain the pooling layers with different scales, as well as different modes, which can extract diversified features with different properties. Afterward, considering the influence of decision boundary on target feature matching, two independent fault classifiers are constructed and trained to decrease the misclassification of the samples. Simultaneously, the local maximum mean discrepancy (LMMD) metric is combined with the domain adversarial network to reduce the difference between the marginal and conditional distributions of samples so as to achieve intraclass and interclass alignment between the source and target domains. Experimental results on the Jiangnan University (JNU) bearing dataset show that, compared with the 1-D-CNN method, the average diagnosis accuracy of the proposed method on the six transfer tasks is increased by 9.43%, which indicates the effectiveness of the proposed fault diagnosis method.