A novel multi-adversarial cross-domain neural network for bearing fault diagnosis. (24th March 2021)

• Record Type: Journal Article
• Authors: Jin, Guoqiang; Xu, Kai; Chen, Huaian; Jin, Yi; Zhu, Changan
• Is Part Of: Measurement science & technology. Volume 32:Number 5(2021)
• Publication Date: 2021-03-24
• Subjects: bearing fault diagnosis -- domain adaptation -- adversarial learning -- mini-max entropy -- deep learning; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/abd900
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Recently, deep neural networks have achieved great success in bearing fault diagnosis. Most existing methods are developed under the assumption that the bearing vibration signals are collected under the same machine operating conditions. However, bearing fault diagnosis under cross-domain conditions will suffer from domain shift problems if the neural network is only trained with the source domain data. Moreover, acquiring enough labeled data from the target domain will be expensive and time-consuming. To address the above problems, this paper proposes an end-to-end multi-adversarial cross-domain neural network for bearing fault diagnosis, which takes labeled source domain data and unlabeled target domain data to achieve the cross-domain bearing fault diagnosis under cross-load conditions and cross-machine conditions. The proposed method employs multi-adversarial training to automatically extract the domain-invariant features from source and target domains instead of manually designing features, which combines domain-adversarial learning and mini-max entropy adversarial learning to adversarially reduce the domain discrepancy between the source and target domains and alleviate the class misalignment problem. The results of the cross-load and the cross-machine experiments prove the effectiveness of the proposed method, and the proposed method provides a promising tool for cross-domain bearing fault diagnosis.