Efficient visual fault detection for freight train braking system via heterogeneous self distillation in the wild

Zhang, Yang; Pan, Huilin; Zhou, Yang; Li, Mingying; Sun, Guodong

doi:10.1016/j.aei.2023.102091

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Control system faults can be quickly and directly identified using knowledge-based fault diagnosis methods. Zhang Y [8] designed an expert system for diagnosing train brake faults, utilizing the acquisition technology for information on the fault status of brake systems of trains. Zhang Y C [9] and Atamuradov V [10] developed an expert system for diagnosing faults in the brake system, which is based on fault trees.…”

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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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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Linear Array Image Alignment Under Nonlinear Scale Distortion for Train Fault Detection

Fu,

Pan,

Zhang

2024

IEEE Sensors J.

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Train Brake System Pipe Leakage Detection and Early Warning Method Based on Bayesian Networks

Hu,

Zhou,

et al. 2023

AJST

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This paper proposes a method for detecting and warning about leaks in train braking system pipelines based on Bayesian networks. Firstly, a detection model for pipeline leaks is established through the learning and inference of Bayesian networks. In the anomaly detection phase, the Bayesian network model is trained using historical data to monitor brake pressure abnormalities in real-time. Secondly, in the parameter regression calibration phase, the location and severity of the pipeline leaks are estimated based on the current brake pressure and relevant parameters. Finally, in the fault inference phase, the Bayesian network model is used to infer the possible causes of the leaks. The effectiveness and reliability of this method are verified through simulation design and actual data analysis. Compared to existing methods, this method can provide accurate leak detection and warning, thereby contributing to the safety of train operation. This research provides an effective method for detecting and warning about leaks in brake system pipelines and has practical application value.

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Efficient visual fault detection for freight train braking system via heterogeneous self distillation in the wild

Cited by 3 publications

References 35 publications

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

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

Linear Array Image Alignment Under Nonlinear Scale Distortion for Train Fault Detection

Train Brake System Pipe Leakage Detection and Early Warning Method Based on Bayesian Networks

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