摘要详情

ID / 提交时间

217 / 2025-06-14 20:57:33

标题

A novel unsupervised domain adaptation method with joint central moment discrepancy for fault diagnosis of rolling bearings

关键字

unsupervised domain adaptation (UDA),joint central moment discrepancy (JCMD),transfer fault diagnosis,effective channel attention mechanism (ECAM),rolling bearings

主题及专题

4、System Monitoring, Fault Diagnosis and Prediction

状态

全文待审

作者

Ran Ren / Kunming University of Science and Technology； Faculty of Mechanical and Electrical Engineering

Tao Liu / Kunming University of Science and Technology；Faculty of Mechanical and Electrical Engineering

Zhenya Wang / Kunming University of Science and Technology；Faculty of Mechanical and Electrical Engineering

Jiabing Gu / Kunming University of Science and Technology；Faculty of Mechanical and Electrical Engineering

摘要

Existing deep learning-based unsupervised domain adaptation (UDA) studies mostly measure only distribution discrepancies of independent feature layers, and the multiple layers of nesting of deep networks can lead to insufficient mining ability of relevant features. A novel UDA transfer diagnosis method with joint central moment discrepancy (JCMD) is proposed. Firstly, an effective channel attention mechanism (ECAM) is embedded into a convolutional neural network (CNN) to extract features related to bearing defect states. Secondly, a loss function based on JCMD is designed to measure joint distribution discrepancies between domains by multiplying central moment discrepancies of hidden activation values across multiple feature layers in both source and target domains. Finally, the effectiveness of the proposed method is validated on two rolling bearing datasets. The findings indicate that the proposed method effectively extracts fault features across various working conditions, enhancing transfer diagnosis performance in unsupervised cross-domain scenarios.