Data-driven fault detection for traction systems of high-speed trains based on segmental autoencoder

Haotong Lv; Mingyue Zhou

doi:10.1117/12.3004597

16 August 2023 Data-driven fault detection for traction systems of high-speed trains based on segmental autoencoder

Haotong Lv, Mingyue Zhou

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870Q (2023) https://doi.org/10.1117/12.3004597
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Faults inevitably occur during high-speed train operations and affect the security of the system. In order to improve train reliability, the paper proposes a fault detection (FD) framework for traction systems based on the segmental autoencoder (SAE), and within this framework, the target fault detection work is implemented in combination with a data-driven method base. The main objective of the proposed scheme is to determine the generalized kernel representation based on the knowledge learned from the autoencoder and to complete the construction of a residual generator by means of a special structure to obtain the final FD results. To verify the FD effect of the method on the traction system, the results are verified by a simulation experimental platform to ensure the effectiveness of the method on the target system.

Citation Download Citation

Haotong Lv and Mingyue Zhou "Data-driven fault detection for traction systems of high-speed trains based on segmental autoencoder", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870Q (16 August 2023); https://doi.org/10.1117/12.3004597

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