摘要详情

ID / 提交时间

97 / 2025-05-14 23:29:55

标题

MCAN-KAN: A Novel Multi-scale Attention End-to-End Network for Fault Diagnosis of Industrial Robots

关键字

Fault diagnosis, Industrial robot, Rotating Machinery, Kolmogorov-Arnold Network (KAN), Feature Fusion, SENet

主题及专题

全体主题

状态

全文待审

作者

Junjie He / Southeast University

Lingfei Mo / Southeast University

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