A Fault Detection Method for Solenoid Valves in Urban Railway Braking Systems Using Temperature-Effect-Compensated Electric Signals

Seo, Boseong; Lee, Guesuk; Jo, Soo-Ho; Oh, Hyunseok; Youn, Byeng D.

doi:10.3795/ksme-a.2016.40.9.835

Cited by 4 publications

(4 citation statements)

References 1 publication

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“…In this paper, a fault diagnostic method is proposed to enable the CBM of urban railway braking systems. According to the previous study (Seo, Lee, Jo, Oh, and Youn, 2016), the most relevant component for the CBM of urban railway braking systems was reported to be solenoid valves. Dominant failure modes of the solenoid valves are shown in Table 1: (1) burnout of the solenoid coil, (2) accumulation of debris, and (3) valve seat damage.…”

Section: Introductionmentioning

confidence: 95%

Solenoid Valve Diagnosis for Railway Braking Systems with Embedded Sensor Signals and Physical Interpretation

Seo

et al. 2016

PHM_CONF

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This paper proposes a fault diagnosis method for solenoid valves in urban railway braking systems. For dominant failure modes of solenoid valves, sensor signals including electrical current, and input and output pressure were acquired and analyzed. The physical behaviors of the solenoid valves are modeled analytically. Numerous forces including magnetic, elastic, and gravity forces are incorporated in the model. With the analytical model and sensor signals, health indices are defined. The health indices are used to quantify the condition of the solenoid valves with different failure modes. Finally, a fault diagnosis method is proposed with the health indices and failure criteria. We anticipate that this study can help decrease maintenance costs and improve reliability of urban railway braking systems.

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Section: Introductionmentioning

confidence: 95%

Solenoid Valve Diagnosis for Railway Braking Systems with Embedded Sensor Signals and Physical Interpretation

Seo

et al. 2016

PHM_CONF

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“…Zhou D H [14] proposed a fault detection and isolation method for brake cylinder systems that utilizes inter-variable variance and reconstruction contribution plots. Seo B [15] suggested a fault diagnosis method for the electromagnetic valves in subway braking systems, which relies on sensor signals and physical behavior models. While these techniques can swiftly identify fault types, their diagnostic accuracy diminishes when dealing with diagnostic objects possessing intricate data structures.…”

Section: Introductionmentioning

confidence: 99%

Research on a Fault Diagnosis Method for the Braking Control System of an Electric Multiple Unit Based on Deep Learning Integration

Wang,

Lin,

et al. 2024

Machines

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A fault diagnosis method based on deep learning integration is proposed focusing on fault text data to effectively improve the efficiency of fault repair and the accuracy of fault localization in the braking control system of an electric multiple unit (EMU). First, the Borderline-SMOTE algorithm is employed to synthesize minority class samples at the boundary, addressing the data imbalance and optimizing the distribution of data within the fault text. Then, a multi-dimensional word representation is generated using the multi-layer bidirectional transformer architecture from the pre-training model, BERT. Next, BiLSTM captures bidirectional context semantics and, in combination with the attention mechanism, highlights key fault information. Finally, the LightGBM classifier is employed to reduce model complexity, enhance analysis efficiency, and increase the practicality of the method in engineering applications. An experimental analysis of fault data from the braking control system of the EMU indicates that the deep learning integration method can further improve diagnostic performance.

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“…As air control valves contain components such as rubber seals and springs, they require a high degree of tightness. Long-term service in harsh operating environments can lead to leakage faults due to seal wear, spring deterioration and mechanical jam, which can lead to functional failures such as braking not relieving and insufficient braking force 1,2 , seriously affecting operation safety, efficiency and maintenance costs of trains.…”

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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A Fault Detection Method for Solenoid Valves in Urban Railway Braking Systems Using Temperature-Effect-Compensated Electric Signals

Cited by 4 publications

References 1 publication

Solenoid Valve Diagnosis for Railway Braking Systems with Embedded Sensor Signals and Physical Interpretation

Solenoid Valve Diagnosis for Railway Braking Systems with Embedded Sensor Signals and Physical Interpretation

Research on a Fault Diagnosis Method for the Braking Control System of an Electric Multiple Unit Based on Deep Learning Integration

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

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