Understanding the Fault in EMU Braking System

Tai, Xiuhua; Guo, Tong; Chen, Maoyin; Zhang, Junfeng; Zhou, Donghua

doi:10.1109/safeprocess45799.2019.9213447

Cited by 2 publications

(1 citation statement)

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“…At present, scholars at home and abroad have performed a lot of research on the fault diagnosis method of the EMU braking system and have achieved fruitful results. Tai et al [3] introduced the structure and composition of the EMU brake control system and the network topology of the fault detection and diagnosis module, proposed a fault detection algorithm, and conducted experiments on a type of EMU brake system in China to verify the effectiveness of the algorithm. Zhou et al [4] proposed a fault detection index with mutual variable variance, completed the feasibility analysis of fault detection, and proposed a fault isolation method.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Model Space Method for Fault Diagnosis of High-Speed Train Air Brake Pipes

Wang

Song

et al. 2023

Applied Sciences

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A data-driven fault diagnosis method is proposed in this study to address the challenge of handling a large volume of pressure data in the air brake pipe of high-speed trains. The suggested method utilizes a BP (back propagation) neural network to transform the time series pressure data into model elements in the model space, ensuring simplicity and stability. Various fitting functions, including Fourier basis, Gaussian basis, polynomial basis, sine basis, and others, are employed to accurately fit the pressure curve of the air brake pipe. The fault diagnosis process involves two steps: classifying the fault based on an optimal approximation equation and diagnosing it by analyzing the topological relationship of the model elements in the model space. The proposed method achieves an average fault diagnosis accuracy of 89.8%, with high accuracy rates for different fault states: 98% for normal state, 88% for blockage state, 84% for leakage state, and 96% for compressor fault state. Compared to the hidden Markov model method, the proposed method improves the average diagnostic accuracy by 2% for known working conditions and 4.87% for all working conditions, demonstrating its effectiveness and reliability. The fault diagnosis of the air brake tube in high-speed trains is of great significance, which aims to realize accurate fault diagnosis and prediction through sensor data monitoring and signal processing technology, so as to ensure the safe operation of high-speed trains. These studies provide an important theoretical and practical basis for the improvement and application of fault diagnosis methods.

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Section: Introductionmentioning

confidence: 99%