报告详情
MCAN-KAN: A Novel Multi-scale Attention End-to-End Network for Fault Diagnosis of Industrial Robots
编号：49
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更新：2025-06-15 10:45:49 浏览：24次
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摘要
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
关键词
Fault diagnosis, Industrial robot, Rotating Machinery, Kolmogorov-Arnold Network (KAN), Feature Fusion, SENet
报告人
Junjie He
student Southeast University稿件作者
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重要日期
-
会议日期
08月01日
2025
至08月04日
2025
-
06月23日 2025
初稿截稿日期
主办单位
中国机械工程学会设备智能运维分会
承办单位
新疆大学
联系方式
- Mrs. Yanruqiang
- ya******@xjtu.edu.cn
历届会议
-
2025年08月01日 中国 wulumuqi
2025 International Conference on Equipment Intelligent Operation and Maintenance -
2023年09月21日 中国 Hefei
第一届（国际）设备智能运维大会
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