Adversarial domain adaptation convolutional neural network for intelligent recognition of bearing faults

•A new ADACNN algorithm based on a weight-sharing CNN is proposed.•The designed ADACNN implements domain adaptation in two different spaces.•An intelligent recognition method is developed for bearing faults. Varying working condition leads to the data distributions offset between training (source do...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2022-05, Vol.195, p.111150, Article 111150
Main Authors: Wu, Yaochun, Zhao, Rongzhen, Ma, Hongru, He, Qiang, Du, Shaohua, Wu, Jie
Format: Article
Language:English
Subjects:
Adaptation
Artificial neural networks
Bearing
Bearings
Convolutional neural network
Domain adaptation
Domains
Fault diagnosis
Faults
Intelligent fault recognition
Intelligent systems
Neural networks
Recognition
Rotating machinery
Training
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Summary:•A new ADACNN algorithm based on a weight-sharing CNN is proposed.•The designed ADACNN implements domain adaptation in two different spaces.•An intelligent recognition method is developed for bearing faults. Varying working condition leads to the data distributions offset between training (source domain) and testing (target domain), which results in insufficient capability of traditional intelligent recognition methods for rotating machinery. Aiming at this problem, a novel intelligent recognition method based on adversarial domain adaptation convolutional neural network (ADACNN) is proposed for bearing faults. First, a weight-sharing convolutional neural network (CNN) is constructed to map training data from both source and target domains to a feature and predicted label spaces, respectively. Then, adversarial learning and maximum mean discrepancy (MMD) are separately introduced in the feature and predicted label space for domain adaptation, and the ADACNN model is established. Finally, a method based on ADACNN, which can realize domain adaptation in two different spaces, is proposed for intelligent recognition of bearing faults. The proposed method is validated by various cross-domain bearing fault recognition tasks under variable speeds and loads. Compared with shallow models, CNN, and commonly used domain adaptation methods, the proposed method has more than a 4% fault recognition accuracy advantage under varying working conditions.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2022.111150