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- W4385877799 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." @default.
- W4385877799 created "2023-08-17" @default.
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- W4385877799 date "2023-08-16" @default.
- W4385877799 modified "2023-10-18" @default.
- W4385877799 title "Data-driven fault detection for traction systems of high-speed trains based on segmental autoencoder" @default.
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- W4385877799 doi "https://doi.org/10.1117/12.3004597" @default.
- W4385877799 hasPublicationYear "2023" @default.
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