Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis

Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis

Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault classes in the early stage of mechanical application....

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 16; p. 5165
Main Authors: Que, Hongbo, Liu, Xuyan, Jin, Siqin, Huo, Yaoyan, Wu, Chengpan, Ding, Chuancang, Zhu, Zhongkui
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 10-08-2024
MDPI
Subjects:
Artificial intelligence
Bearings
Classification
Datasets
Deep learning
deep transfer learning
domain adaptation
Efficiency
Fault diagnosis
Fourier transforms
label imbalance
Machine learning
Morphology
Neural networks
partial transfer learning
Wavelet transforms
Working conditions
label imbalance
fault diagnosis
partial transfer learning
domain adaptation
deep transfer learning
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Summary:Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault classes in the early stage of mechanical application. The more likely situation is that the training data in the target domain only contain a subset of the entire health state, which will lead to the problem of label imbalance compared with the source domain. The outlier classes in the source domain that do not have corresponding target domain samples for feature alignment will interfere with the feature transfer of other classes. To address this specific challenge, this study introduces an innovative inter-class feature transfer fault diagnosis approach. By leveraging label information, the method distinctively computes the distribution discrepancies among shared classes, thereby circumventing the deleterious influence of outlier classes on the transfer procedure. Empirical evaluations on two rolling bearing datasets, encompassing multiple partial transfer tasks, substantiate that the proposed method surpasses other approaches, offering a novel and efficacious solution for the realm of intelligent bearing fault diagnosis.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24165165