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A zero-sample intelligent fault diagnosis method for bearings based on category relationship model

Published: 02 July 2024 Publication History

Abstract

Collecting data containing all categories is crucial for fault diagnosis methods based on machine learning, which can be difficult in real industrial scenarios, particularly under new working condition. Therefore, a zero-sample intelligent fault diagnosis method is proposed for the bearings working in new condition. Specifically, a health-fault category relationship model among the different categories is developed using improved sparsity-constrained generative adversarial network. The fault data in new working condition can be generated by the model. Then, a fault diagnosis model in new working condition is established using transfer learning between existing working condition and new working condition. Finally, two bearing datasets are used to verify the proposed method. The results show that the model is effective for the bearings under new working condition without fault data, especially for the big difference of data distribution caused by the changing working conditions. It indicates that the proposed method has great potential in practical industrial applications.

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Published In

cover image Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence  Volume 130, Issue C
Apr 2024
1523 pages

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 02 July 2024

Author Tags

  1. Fault diagnosis
  2. Zero-sample
  3. Health-fault category relationship model
  4. Domain adaptation

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