Abstract Details
50 / 2023-08-30 10:24:13
Intelligent Edge Gearbox Faults Diagnosis System via Multiscale Depthwise Separable Convolution Network
gearbox,depthwise separable convolution,fault diagnosis system,edge computing
Final Paper
The health status monitoring of key components in gearbox is of great significance to ensure the safe operation as well as the stability of production efficiency. However, the real-time requirements of the fault diagnosis system and the model size of the deep learning approach limit the practical application of edge intelligent fault diagnosis in gearboxes. Therefore, by combining deep learning methods method and edge computing technology, this study proposes an intelligent edge diagnosis system for gearbox. First, the architecture of the edge fault diagnosis system is designed based on wireless sensor networks. Second, a lightweight model multiscale depthwise separable convolution network (MKDS_1DCNN) is proposed to enhance the extraction of vibration signal features. Finally, the MKDS_1DCNN is deployed on the edge node with X-CUBE-AI edge inference framework, including signal acquisition circuit and STM32. The conducted experimental studies show that this implementation can achieve an inference time of less than 20 ms and accuracy of more than 98 %. The effectiveness of the proposed gearbox edge fault diagnosis system and its capability in practical applications at the edge are verified in the experiments.
Important Date
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Conference Date
Nov 02
2023
toNov 04
2023
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Dec 15 2023
Draft paper submission deadline
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Dec 20 2023
Registration deadline
Sponsored By
IEEE Instrumentation and Measurement Society
Xidian University
Xidian University
Contact Information
Previous Conferences
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2022-11-30 China Harbin
International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence -
2021-10-21 China Nanjing
2021 International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence -
2020-10-15 China Xi'an
International Conference on Sensing, Measurement and Data Analytics in the era of Artificial Intelligence
