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A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

Author

Listed:
  • Cheng Peng

    (School of Computer, Hunan University of Technology, Zhuzhou 412007, China
    School of Automation, Central South University, Changsha 410083, China)

  • Lingling Li

    (School of Computer, Hunan University of Technology, Zhuzhou 412007, China)

  • Qing Chen

    (School of Computer, Hunan University of Technology, Zhuzhou 412007, China)

  • Zhaohui Tang

    (School of Automation, Central South University, Changsha 410083, China)

  • Weihua Gui

    (School of Automation, Central South University, Changsha 410083, China)

  • Jing He

    (School of Computer, Hunan University of Technology, Zhuzhou 412007, China)

Abstract

Fault diagnosis under the condition of data sets or samples with only a few fault labels has become a hot spot in the field of machinery fault diagnosis. To solve this problem, a fault diagnosis method based on deep transfer learning is proposed. Firstly, the discriminator of the generative adversarial network (GAN) is improved by enhancing its sparsity, and then adopts the adversarial mechanism to continuously optimize the recognition ability of the discriminator; finally, the parameter transfer learning (PTL) method is applied to transfer the trained discriminator to target domain to solve the fault diagnosis problem with only a small number of label samples. Experimental results show that this method has good fault diagnosis performance.

Suggested Citation

  • Cheng Peng & Lingling Li & Qing Chen & Zhaohui Tang & Weihua Gui & Jing He, 2021. "A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets," Energies, MDPI, vol. 14(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:944-:d:497546
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    Cited by:

    1. Jannis N. Kahlen & Michael Andres & Albert Moser, 2021. "Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault," Energies, MDPI, vol. 14(20), pages 1-20, October.

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