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Imbalance domain adaptation network with adversarial learning for fault diagnosis of rolling bearing

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Abstract

Domain adaptation has been widely used in industrial fault diagnosis. However, it requires a balance of data distribution between the source domain and the target domain. Unfortunately, cross-domain balanced distribution is not common in actual applications, bringing difficulties to the practical application of domain adaptation. In the article, we focus on this difficult problem and propose a new imbalance domain adaptation network with adversarial learning (IDAL). The model applies adversarial learning to data augmentation of the target domain and uses the domain adaptation based on a neural network to narrow the feature distribution discrepancy between the source and target domains. Ultimately, the parameters are transferred to the target domain and fine-tuned with a small number of labeled samples so as to achieve fault diagnosis. The accuracy of the proposed method on two data sets is more than 98%. It is worth noting that common deep learning networks can be embedded in IDAL, so the model can be widely used in different industrial scenarios.

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Availability of data and material

The datasets analyzed during this study come from the following public domain resource: https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website. The data supporting the results of this article are included within the article.

Code availability

The code during the study is proprietary and limited.

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Funding

This work was supported by the National Key R&D Program of China under Grant 2019YFE0105300.

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, China

    Hongqiu Zhu, Ziyi Huang, Fei Cheng & Can Zhou

  2. School of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Biliang Lu

Authors
  1. Hongqiu Zhu
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  2. Ziyi Huang
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  3. Biliang Lu
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  4. Fei Cheng
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  5. Can Zhou
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Contributions

HZ contributed to the conception of the study; ZH performed the experiment; BL contributed significantly to analysis and manuscript preparation; FC performed the data analyses and wrote the manuscript; CZ helped perform the analysis with constructive discussions.

Corresponding author

Correspondence to Can Zhou.

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The authors have no relevant financial or non-financial interests to disclose.

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All the experimental subjects in this study do not include any animals or people and do not violate ethics.

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The author agrees to publication in the International Journal of Advanced Manufacturing Technology and also to publication of the article in English.

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Zhu, H., Huang, Z., Lu, B. et al. Imbalance domain adaptation network with adversarial learning for fault diagnosis of rolling bearing. SIViP 16, 2249–2257 (2022). https://doi.org/10.1007/s11760-022-02190-7

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  • DOI: https://doi.org/10.1007/s11760-022-02190-7

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