Multisource weighted domain adaptive network for bearing fault diagnosis

Yingjie Huang; Qiang Fu

doi:10.1117/12.3015441

15 March 2024 Multisource weighted domain adaptive network for bearing fault diagnosis

Yingjie Huang, Qiang Fu

Proceedings Volume 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023); 1307918 (2024) https://doi.org/10.1117/12.3015441
Event: 3rd International Conference of Testing Technology and Automation Engineering (TTAE 2023), 2023, Xi-an, China

Abstract

Bearing failure is one of the common faults in rotating mechanical systems, significantly impacting the reliability and performance of the machinery. However, due to variations in operating conditions and environmental factors, bearing failure data exhibits a multi-source domain issue, posing challenges for traditional fault diagnosis methods to be effectively applied. In this paper, a new multi-source weighted domain adaptive (MSWDA) framework is proposed, which is implemented by feature weighted and multi-layer feature extraction structure. To achieve feature-level distribution alignment, the minimum slice Wasserstein distance is used as a measure to measure the difference in feature distribution. Extensive experiments are conducted on the CWRU bearing datasets to validate the effectiveness of the proposed model.

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

Citation Download Citation

Yingjie Huang and Qiang Fu "Multisource weighted domain adaptive network for bearing fault diagnosis", Proc. SPIE 13079, Third International Conference on Testing Technology and Automation Engineering (TTAE 2023), 1307918 (15 March 2024); https://doi.org/10.1117/12.3015441

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KEYWORDS

Feature extraction

Data modeling

Performance modeling

Diagnostics

Visualization

Education and training

Data processing

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