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A Multi-stage Multi-scale Feature Fusion Network Based on KAN for Industrial Robot Fault Diagnosis
ID：123 View Protection：ATTENDEE Updated Time：2025-11-10 15:45:51 Hits：166 Poster Presentation

Start Time：Pending（Asia/Shanghai）

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Abstract
With the rapid advancement of China's manufacturing sector, industrial robots have assumed a pivotal role within the industry. The operational status of these robots directly impacts a company's production efficiency. However, the complex working environments of industrial robots pose significant challenges for existing deep learning models in extracting critical information from signals. To address this issue, this paper proposes a multi-stage, multi-scale feature fusion network based on KAN. Firstly, one-dimensional signals are transformed into wavelet time-frequency maps, and a multi-scale information flow is designed based on LSConv. Secondly, to fully extract critical features from the multi-scale information flow, an attention-based LSConv feature enhancement layer is designed. Subsequently, to leverage features from multiple network stages, a multi-scale feature enhancement layer is implemented. Finally, KAN is employed as a classifier to further enhance the network's diagnostic capabilities. Experimental results on the SDUST dataset demonstrate that the proposed method outperforms existing fault diagnosis approaches.
Keywords
Kolmogorov-Arnold network(KAN),Fault Diagnosis,Attention mechanism,Multi-stage Feature,Multi-scale feature
Speaker
Junjie He
Doctoral student Southeast University

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

  • Conference Date

    Nov 21

    2025

    to

    Nov 23

    2025

  • Oct 20 2025

    Draft paper submission deadline

  • Dec 08 2025

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
Organized By
South China University of Technology
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