Unsupervised machine learning techniques to prevent faults in railroad switch machines

Soares, Nielson; Aguiar, Eduardo Pestana de; Souza, Amanda Campos; Goliatt, Leonardo

doi:10.1016/j.ijcip.2021.100423

Cited by 12 publications

(2 citation statements)

References 33 publications

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“…Railroad switch machines are an essential equipment in a railway system. Nielson Soares et al propose a predictive model based on computational intelligence techniques to operate in the vicinity of the ones classified as faults and increase productivity [8]. E.P.…”

Section: Risk Factors Associated To Railway Operational Accidentsmentioning

confidence: 99%

Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

et al. 2022

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The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation.

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Section: Risk Factors Associated To Railway Operational Accidentsmentioning

confidence: 99%

Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

et al. 2022

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“…Zhang T [12] combined fuzzy theory with the analytic hierarchy process to diagnose faults and perform a comprehensive evaluation of the CCBII braking system. Soares N [13] introduced a feature extraction method that employs scalable hypothesis testing to select, extract, and cluster features using unsupervised models with principal component analysis. Zhou D H [14] proposed a fault detection and isolation method for brake cylinder systems that utilizes inter-variable variance and reconstruction contribution plots.…”

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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Machine Learning to Predict Railway Infrastructure Defects

Elansari,

Idrissi,

Tifernine

2024

Studies in Computational Intelligence

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Unsupervised machine learning techniques to prevent faults in railroad switch machines

Cited by 12 publications

References 33 publications

Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks

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

Machine Learning to Predict Railway Infrastructure Defects

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