A Variable Load Fault Detection and Diagnosis Method for Traction Systems of High-speed Trains

The traction systems of high-speed trains operate under variable load conditions, which induce significant variations in system data characteristics. Traditional methods usually use a unified global model to describe the traction system, but this approach easily ignores the local features of the dat...

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Vydané v:	International journal of control, automation, and systems Ročník 23; číslo 9; s. 2599 - 2610
Hlavní autori:	Li, Xuedong, Wang, Hongzhi, Wan, Zhiwei, Cheng, Chao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.09.2025 Springer Nature B.V 제어·로봇·시스템학회
Predmet:	Control Engineering False alarms Fault detection Fault diagnosis High speed rail Load Mechatronics Methods Neural networks Nonlinear systems Regular Papers Robotics System effectiveness 제어계측공학 traction systems of high-speed trains Conditional variational autoencoder (CVAE) fault detection and diagnosis (FDD) variable loads
ISSN:	2005-4092, 1598-6446, 2005-4092
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Abstract	The traction systems of high-speed trains operate under variable load conditions, which induce significant variations in system data characteristics. Traditional methods usually use a unified global model to describe the traction system, but this approach easily ignores the local features of the data, resulting in an increase in false alarm rate. Therefore, this paper proposes a new data-driven FDD method based on a conditional variational autoencoder (CVAE) to address this challenge. The key advantages of the proposed method include: 1) The proposed method significantly improves the sensitivity and reliability of fault detection under variable loads. 2) The proposed FDD framework does not require precise physical models or system-specific parameters, making it highly adaptable. 3) The proposed method can be readily extended to other nonlinear industrial systems. The effectiveness of the proposed method is validated on a traction system of a high-speed train simulation platform.
AbstractList	The traction systems of high-speed trains operate under variable load conditions, which induce significant variations in system data characteristics. Traditional methods usually use a unified global model to describe the traction system, but this approach easily ignores the local features of the data, resulting in an increase in false alarm rate. Therefore, this paper proposes a new data-driven FDD method based on a conditional variational autoencoder (CVAE) to address this challenge. The key advantages of the proposed method include: 1) The proposed method significantly improves the sensitivity and reliability of fault detection under variable loads. 2) The proposed FDD framework does not require precise physical models or system-specific parameters, making it highly adaptable. 3) The proposed method can be readily extended to other nonlinear industrial systems. The effectiveness of the proposed method is validated on a traction system of a high-speed train simulation platform. The traction systems of high-speed trains operate under variable load conditions, which induce significant variations in system data characteristics. Traditional methods usually use a unified global model to describe the traction system, but this approach easily ignores the local features of the data, resulting in an increase in false alarm rate. Therefore, this paper proposes a new data-driven FDD method based on a conditional variational autoencoder (CVAE) to address this challenge. The key advantages of the proposed method include: 1) The proposed method significantly improves the sensitivity and reliability of fault detection under variable loads. 2) The proposed FDD framework does not require precise physical models or system-specific parameters, making it highly adaptable. 3) The proposed method can be readily extended to other nonlinear industrial systems. The effectiveness of the proposed method is validated on a traction system of a high-speed train simulation platform. KCI Citation Count: 0
Author	Cheng, Chao Li, Xuedong Wan, Zhiwei Wang, Hongzhi
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SubjectTerms	Control Engineering False alarms Fault detection Fault diagnosis High speed rail Load Mechatronics Methods Neural networks Nonlinear systems Regular Papers Robotics System effectiveness 제어계측공학
Title	A Variable Load Fault Detection and Diagnosis Method for Traction Systems of High-speed Trains
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