摘要详情

ID / 提交时间

50 / 2023-08-30 10:24:13

标题

Intelligent Edge Gearbox Faults Diagnosis System via Multiscale Depthwise Separable Convolution Network

关键字

gearbox,depthwise separable convolution,fault diagnosis system,edge computing

主题及专题

Special Sessions > SS04 - Application of Computational Intelligence in Manufacturing and Logistics

状态

终稿

作者

Shengbao Qin / Chongqing University

Yuanyue Pu / Chongqing University

Jian Tang / chongqing university

Shengpei Yao / Chongqing University

Kaiqi Chen / Chongqing University

Wenbin Huang / Chongqing University

摘要

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.