Digital Twin-Driven Graph Convolutional Memory Network for Defect Evolution Assessment of Rolling Bearings

计算机科学 图形 理论计算机科学
作者
Yongchang Xiao,Lingli Cui,Dongdong Liu,Xin Pan
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:73: 1-10 被引量:2
标识
DOI:10.1109/tim.2024.3385830
摘要

The quantitative diagnosis of rolling bearing defects still mainly relies on the manual analysis of vibration signals, and limited to a specific moment in time, which restricts the intelligent identification of life-cycle defect evolution. In this paper, a novel digital twin-driven Graph Convolutional Memory Network (GCMN) is proposed for evaluating the defect evolution of rolling bearings throughout the whole life. In the proposed method, a dynamic twin model is constructed to generate the vibration responses that characterize the state of bearings. The twin model is capable of accurately simulating the operational conditions of the bearing and interacting with the actual responses, thereby enhancing the accuracy of the model. In addition, a graph network model GCMN is developed to transfer knowledge from the twin model to physical entity through domain adaptation, thereby revealing the relationship between vibration responses and defect sizes. It extracts spatial features through nonlinear transformation of graph data, and incorporates temporal features via the hidden layer state at the previous moment. The experimental results demonstrate that the proposed method accurately characterizes the local defect extension throughout the bearing entire lifespan.
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