报告详情
A Lightweight Network Framework for Fault Diagnosis of Quadruped Robot Joint Modules
编号：59
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更新：2024-10-23 10:44:16
浏览：188次
口头报告
报告开始：2024年11月01日 14:20（Asia/Shanghai）
报告时间：20min
所在会场：[P3] Parallel Session 3 [P3-1] Parallel Session 3（November 1 PM）
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摘要
With the widespread deployment of quadruped robots across various application scenarios, they are required to adapt to diverse motion patterns when performing different tasks. This necessity imposes complex stresses and loads on the components within the robot's joint modules during movement, leading to a higher risk of wear and failure. To address these challenges, this study proposes a novel lightweight multi-scale neural network based on a dual attention mechanism and Depthwise Separable Convolution (DSconv), named Atten-MSDsN, for diagnosing faults in joint modules. This approach captures critical fine-grained features across the global scope of the signal through the attention mechanism, extracts multi-scale features from fault signals using convolution kernels of varying sizes, and reduces the number of parameters in the feature extractor via DSconv. Experimental analysis demonstrates that the Atten-MSDsN framework offers the advantages of being lightweight and robust.
关键词
Fault diagnosis,Dual attention mechanisms,Deeping learning,Depthwise separable convolution,Quadruped robot joint modules
报告人
ShiMaoping
student Shanghai University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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