Fault Feature Extraction of Relay Valve Leakage Based on Test for Standard EMU Brake System

Zuo, Jianyong; Ding, Jingxian; Peng, Shun; Guo, Hu; Zhao, Tiefeng; Li, Jialiang

doi:10.12783/dtetr/amme2017/19533

Cited by 2 publications

(2 citation statements)

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“…The abnormality of the relay valve leakage is mainly the statistical mean shift [22]. Therefore, the simulation test in this paper has two hypothesis H 0 and H 1 .…”

Section: Sequential Probability Ratio Testmentioning

confidence: 96%

Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

2019

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To identify and diagnose the latent leakage faults of key pneumatic units in the Chinese standard Electric Multiple Units (EMU) braking system, a multi-source information fusion method based on Kalman filtering, sequential probability ratio test (SPRT), and support vector machine (SVM) is proposed. The relay valve is taken as an example for research. Firstly, Kalman's state estimation function is used to obtain the innovation sequence, and the innovation sequence is input into the SPRT model to help recognize latent leakage faults of the relay valve. Using this method, the problem of the incomplete training set of the traditional SPRT method due to the change of the braking level and the vehicle load is solved. Secondly, the eight time-domain parameters of the relay valve input and the output pressure signal are extracted as fault characteristics, and then input to the support vector machine to realize the internal and external leakage fault diagnosis of the relay valve, which provides a reference for maintenance. Finally, this method is verified by the fault simulation data by quickly identifying latent leakage faults and diagnosing the internal and external leakage at a fault recognition rate of 100% by SVM under small sample conditions. Appl. Sci. 2019, 9, 300 2 of 13 abnormalities. Compared to conventional threshold detection methods, SPRT requires the smallest average number of samples for the fault detection of signals, which means that a developmental fault signal can be indicated earlier with the same accuracy. Meanwhile, considering the particularity of the braking system-that is, the braking force required by the vehicle under a different vehicle load and braking level is different-it is difficult for the traditional SPRT to prepare a complete training set. In this paper, the Kalman predictor is used to preprocess the pressure signal, and the innovation sequence obtained by Kalman filtering is used as the detection signal of SPRT.Due to the distinct impacts of different leakage faults, this paper extracts fault characteristic parameters to realize the fault diagnoses of relay valve leakages, to provide guidance for maintenance decisions and save maintenance costs. At present, the most efficient diagnostic methods for hydraulic (pneumatic) system faults mainly include the fault tree [10], the Back Propagation (BP) neural network [11][12][13], and the expert system [14]. Among them, the fault tree has great limitations in online diagnosis. The BP neural network method has a slow convergence speed, which lengthens the training time, and makes it easy to get the local minimum; conversely, the amount of data required by the expert system is relatively large, and the diagnostic results can be inaccurate. The support vector machine (SVM) [15,16] algorithm has special advantages in small sample, high dimensional, and nonlinear problems. From a theoretical point of view, the SVM algorithm obtains the global optimal solution, which makes up for the fact that the BP neural network method can easily fall into the l...

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“…The abnormality of the relay valve leakage is mainly the statistical mean shift [22]. Therefore, the simulation test in this paper has two hypothesis H 0 and H 1 .…”

Section: Sequential Probability Ratio Testmentioning

confidence: 96%

Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

2019

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“…Leakage faults cannot be detected until serious leakage occurs and affects the function of the braking system. However, latent leakage faults already exist and there is an urgent need to design online monitoring algorithms for leakage fault warning, and the key lies in fault characteristic extraction 3,4 . Fault characteristic extraction needs to be based on fault data [5][6][7] , but fault data of train braking system is missing, while physical tests are costly and inefficient.…”

Section: Introductionmentioning

confidence: 99%

Analysis of leakage fault characteristics of air control valves in train braking system

Ding

Zuo

2023

Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022)

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In order to monitor and warn the leakage fault of air control valves in train braking system, rapid analysis of fault characteristics is the key premise. The relay valve used in train braking system was taken as an example in this paper. Its working principle and physical structure were clarified, and the leakage fault was divided into internal leakage and external leakage based on the airflow direction. The simulation model of relay valve was established, and the internal and external leakage faults were simulated by using the model-based fault injection method. The influence laws of leakage faults on the output pressure and balance state of the relay valve were analyzed, and it was found that the relay valve had a dynamic balance position besides the tri-state position. Finally, the leakage simulation bench test was carried out, and the test results show that the relay valve model could correctly analyze the leakage fault characteristics, and the output pressure could be used as the fault characteristic to warn leakage faults. The method proposed in this paper provides an effective way to analyze the fault characteristics of braking system and can be used to obtain fault data for fault diagnosis.

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Fault Feature Extraction of Relay Valve Leakage Based on Test for Standard EMU Brake System

Cited by 2 publications

References 0 publications

Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

Latent Leakage Fault Identification and Diagnosis Based on Multi-Source Information Fusion Method for Key Pneumatic Units in Chinese Standard Electric Multiple Units (EMU) Braking System

Analysis of leakage fault characteristics of air control valves in train braking system

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