Fault detection based on locally linear embedding for traction systems in high-speed trains

Fangcheng Dou; Yunfei Ju; Chao Cheng

doi:10.1117/12.3055572

12 December 2024 Fault detection based on locally linear embedding for traction systems in high-speed trains

Fangcheng Dou, Yunfei Ju, Chao Cheng

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134391D (2024) https://doi.org/10.1117/12.3055572
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

This paper presents a fault detection method based on locally linear embedding (LLE) for the high-speed train traction systems. The method maps high-dimensional complex data into low-dimensional data, analyzes the spatial characteristics of these data within their local neighborhoods, and achieves overall system state monitoring and fault prediction. The paper provides a detailed introduction to the theoretical foundation of LLE fault detection, including techniques such as local linear embedding, fault feature extraction, and analysis, and discusses the roles of these techniques in practical applications. Through the construction of simulation models and experimental data, the effectiveness and robustness of the proposed method are verified.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fangcheng Dou, Yunfei Ju, and Chao Cheng "Fault detection based on locally linear embedding for traction systems in high-speed trains", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134391D (12 December 2024); https://doi.org/10.1117/12.3055572

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