Unsupervised Deep Transfer Learning Method for Rolling Bearing Fault Diagnosis Based on Improved Convolutional Neural Network

Abstract

As a critical component widely used in mechanical equipment, the implementation of intelligent fault diagnosis for rolling bearings can improve the reliability of equipment. In this paper, a method named JMMD-CKDSCNet is proposed to address the task of fault diagnosis under unsupervised domain discrepancy scenarios. First, the convolutional kernel dropout (CKD) mechanism is introduced in the convolutional layer, and partial convolutional kernel weights are set to be inactive during the training process using the random mask. Second, skip connection (SC) fuses the features of multiple shallow layers to preserve and transfer the original features. Finally, domain alignment is achieved using joint maximum mean discrepancy (JMMD), which measures the joint distribution between different domains with feature discrepancies under the condition that the target domain lacks labeled data. The experimental results demonstrate that CKDSCNet exhibits superior generalization performance and outperforms other models in terms of diagnostic accuracy and model performance. Compared with other domain adaptation methods, JMMD has significant superiority, proving the application value of JMMD-CKDSCNet.