Research on gear cross-condition fault diagnosis based on transfer learning

Jiazhi Li; Jianchun Lin

doi:10.1117/12.3046547

27 September 2024 Research on gear cross-condition fault diagnosis based on transfer learning

Jiazhi Li, Jianchun Lin

Author Affiliations +

Proceedings Volume 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024); 132613E (2024) https://doi.org/10.1117/12.3046547
Event: 10th International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 2024, Wuhan, China

Abstract

This paper proposes a method for cross-condition fault diagnosis of gears that combines the Convolutional Block Attention Module (CBAM) with subdomain adaptation networks. The channel submodule employs both the maximum pooling output and the average pooling output from the shared network. Similarly, the spatial submodule utilizes analogous outputs, which are pooled across the channel axis and then passed to convolutional layers. The advanced subdomain adaptation network enhances the functionality of the Deep Adaptation Network (DAN) by capturing precise information for each category, synchronizing the overall distributions of source and target domains, and coordinating the distributions of relevant subdomains. Through comparative experiments, our proposed method outperforms Convolutional Neural Networks (CNN), Deep Adaptation Network (DAN), and Deep Subdomain Adaptation Network (DSAN).

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiazhi Li and Jianchun Lin "Research on gear cross-condition fault diagnosis based on transfer learning", Proc. SPIE 13261, Tenth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2024), 132613E (27 September 2024); https://doi.org/10.1117/12.3046547

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