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A Novel Transfer Capsule Network Based on Domain-Adversarial Training for Fault Diagnosis

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Abstract

The success of intelligent fault diagnosis comes from one important assumption: the test data are consistent with the training data in data distribution. However, in the actual factory environment, the difference in data distribution due to changing working conditions will cause the performance of the trained model to seriously degrade. To address the problems, a transfer capsule network based on domain-adversarial training (DATTCN) is proposed. Specifically, it extracts fault features through wide convolution and multi-scale convolution, and performs fault classification through capsule networks. And the purpose of enhancing the diagnostic performance of the target domain is realized through adversarial training. In the fault identification of the Case Western Reserve University data set under varying working conditions, the DATTCN algorithm almost reaches 100% accuracy, and it is 92.3% on the Paderborn University data set. The accuracy of the DATTCN algorithm exceeds other advanced algorithms, fully verifying the effectiveness of the DATTCN algorithm.

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Data availability

The dataset generated in this paper is available from the corresponding author on reasonable request.

Code availability

The custom software code generated during the current study is not publicly available due to confidentiality policy.

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Funding

This work was supported by Shanghai Informatization Development Special Project (Grant No. 202001012), Shanghai Industrial Internet Innovation and Development Project (Grant No. 2020-GYHLW-02010), and Science and Technology Project Fund of East China Branch of State Grid Corporation (Grant No. ZWDL211578).

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Authors and Affiliations

  1. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 201210, China

    Yu Wang, Dejun Ning & Junzhe Lu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yu Wang

Authors
  1. Yu Wang
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  2. Dejun Ning
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  3. Junzhe Lu
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Contributions

YW: Conceptualization, methodology, software, visualization, writing—original draft, writing—review and editing. DN: Conceptualization, visualization, writing—review and editing, funding acquisition. JL: Software, writing—review and editing.

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Correspondence to Dejun Ning.

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Wang, Y., Ning, D. & Lu, J. A Novel Transfer Capsule Network Based on Domain-Adversarial Training for Fault Diagnosis. Neural Process Lett 54, 4171–4188 (2022). https://doi.org/10.1007/s11063-022-10803-y

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