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MCAN-KAN: A Novel Multi-scale Attention End-to-End Network for Fault Diagnosis of Industrial Robots
ID：49 View Protection：ATTENDEE Updated Time：2025-06-15 10:45:49 Hits：249 Poster Presentation

Start Time：Pending（Asia/Shanghai）

Duration：Pending

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Abstract
Industrial robots play a significant role in the current industry. However, as industrial robots operate in complex industrial environments, crucial defect characteristics are tough to discover using conventional deep learning algorithms. To extract key fault features and achieve industrial robots' state monitoring, the study provides a multiscale convolutional attention networks based on KAN (MCAN-KAN). Firstly, to extract rich features, a multi-scale feature fusion layer was designed. Secondly, to enhance the multi-scale fusion features, a dual-scale feature enhancement layer was designed. To fully utilize the capabilities of the two stages, a multi-stage attention characteristic interaction layer was created. Finally, KAN is used as the classifier to further improve the diagnostic performance of MCAN-KAN. The reliability of the MCAN-KAN approach has been verified utilizing the SDUST dataset. Experiments show. MCAN-KAN is superior to the existing intelligent fault diagnosis algorithms
Keywords
Fault diagnosis, Industrial robot, Rotating Machinery, Kolmogorov-Arnold Network (KAN), Feature Fusion, SENet
Speaker
Junjie He
student Southeast University

Submission Author
Junjie He Southeast University
Lingfei Mo Southeast University
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Important Date

  • Conference Date

    Aug 01

    2025

    to

    Aug 04

    2025

  • Aug 20 2025

    Draft paper submission deadline

Sponsored By
中国机械工程学会设备智能运维分会
Organized By
新疆大学
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