Online Monitoring Technology for Gear Transmission Systems Driven by Digital Twin under Edge-Cloud Collaboration

doi:10.3901/JME.260559

Abstract

Abstract: Conventional evaluation methods of structural performance in gear-transmission systems rely on offline finite-element analysis, which is time-consuming and cannot satisfy the timeliness requirements of online condition monitoring in high-end equipment. To overcome this limitation, an edge-cloud collaborative digital-twin technique is proposed for online condition monitoring. A digital-twin framework containing physical, edge, cloud, and communication layers is established with clearly defined function and road-map of each layer. A graph neural network-based surrogate model is developed to rapidly predict structural performance, addressing the unstructured mesh topology of finite-element models. An online condition monitoring platform is implemented on a gear transmission test bench, integrating real-time signal acquisition, structural prediction, and cloud-based visualization. Results demonstrate that the surrogate model achieves more than 90% consistency with offline finite-element analyses with a total response time within 4 s, enabling a rapid and accurate structure monitoring of core components in gear transmission systems, and providing a feasible new approach for real-time condition monitoring of high-end equipment.

Key words: gear transmission system, online monitoring, digital twin, edge-cloud collaboration, graph network

CLC Number:

TH132
TP391

ZHU Benran, CHAO Qun, WANG Zhongrui, LIU Chengliang. Online Monitoring Technology for Gear Transmission Systems Driven by Digital Twin under Edge-Cloud Collaboration[J]. Journal of Mechanical Engineering, 2026, 62(12): 87-97.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260559

http://www.cjmenet.com.cn/EN/Y2026/V62/I12/87

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