Application of Edge Computing Based on Multi-Scale Convolutional Neural Networks in Gear Fault Diagnosis
ID：25
Submission ID：29 View Protection：ATTENDEE
Updated Time：2024-10-23 10:46:50
Hits：43
Oral Presentation
Start Time：2024-11-01 15:00 （Asia/Shanghai）
Duration：20min
Session：[P3] Parallel Session 3 » [P3-1] Parallel Session 3（November 1 PM）
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Abstract
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.
Keywords
gear fault duagnosis,multi-scale feature,edeg computing,convolutional neural network
Speaker
ChenTianming
Mr Nanjing University of Science and TechnologySubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
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