报告详情
Application of Edge Computing Based on Multi-Scale Convolutional Neural Networks in Gear Fault Diagnosis
编号：25
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更新：2024-10-23 10:46:50
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报告开始：2024年11月01日 15:00（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
Gear fault diagnosis plays a crucial role in detecting gear faults promptly, enabling timely repairs or replacements to minimize potential losses. While deep learning-based methods have been widely used for gear fault diagnosis, their effective application in real-world industrial environments remains a challenge. In this paper, we introduce a novel approach that combines a multi-scale convolutional neural network with multi-knowledge distillation (MSCNN-MKD). This approach is deployed on edge computing nodes for gear fault diagnosis. Experiments results validate the effectiveness of our proposed methodology. The proposed method achieves an accuracy of 98.76% in recognizing 7 fault categories. Additionally, the number of parameters is 18.54K, the floating-point operations are 41.54M, and the average inference time is 1.07ms. The methodology has great potential for practical industrial applications in gear faults diagnosis.
关键词
gear fault duagnosis,multi-scale feature,edeg computing,convolutional neural network
报告人
ChenTianming
Mr Nanjing University of Science and Technology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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