A Digital Twin-Based Fault Diagnosis Framework for Bogies of High-Speed Trains

作者：Xingtang Wu, Wenbo Lian, Min Zhou, Haifeng Song, Hairong Dong · 发表于：IEEE Journal of Radio Frequency Identification · 年份：2022 · DOI：10.1109/jrfid.2022.3216331 · 被引用次数：44 · 研究领域：Machine Fault Diagnosis Techniques、Reliability and Maintenance Optimization、Welding Techniques and Residual Stresses

To improve the safety of High-speed trains’ operation and reduce the bogie maintenance cost and failure rate, this paper proposes a digital twin-based framework for fault diagnosis of bogies. A digital twin system of the bogie is constructed from seven dimensions. Then a framework for the fault diagnosis of the high-speed train bogie is proposed, in which a multi-layer convolutional neural network is adopted for fault diagnosis.