Detection of Railroad Anomalies using Machine Learning Approach

Nugraha, Ade Chandra; Supangkat, Suhono Harso; Nugraha, I Gusti Bagus Baskara; Trimadi, Harno; Purwadinata, Awan Hermawan; Sumarni, .; Sundari, S

doi:10.1109/iciss53185.2021.9533226

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…To detect irregularities of insulator breakage in high-speed railway catenaries, Gong et al 14 suggested a deep learning-based technique mixed with semantic segmentation technology. Although Nugraha et al 15 suggested using ML to precisely identify railroad irregularities and forecast the state of vital components. Yet, these techniques may greatly benefit from advancement.…”

Section: Foundation Workmentioning

confidence: 99%

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Employing a hybrid model based on texture-biased convolutional neural networks and edge-biased vision transformers for anomaly detection of signal bonds

2023

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The railway system of Japan plays a vital role in the national transportation network.A key issue in public transport safety is anomaly detection in railways. Lately, developing robust algorithms and methods for anomaly detection has become the premier task in this field. Recently introduced approaches based on convolutional neural networks, generative adversarial networks, and vision transformers (ViTs) have remarkably improved the research in anomaly detection. Our work proposes a high-performance module for the anomaly detection of signal bonds. First, we present an overview of the proposed module; then, the object detection model and the proposed hybrid classification model based on texture-biased convolutional neural networks and edge-biased ViTs are introduced. Finally, the proposed anomaly detection module is evaluated for accuracy using the dataset from the East Japan Railway Company and the receiver operating characteristic curve. The results show that our proposed module achieves a better performance on real-life data than the two methods that were combined to generate it, raising hope toward possible usage in other areas as well.

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Section: Foundation Workmentioning

confidence: 99%

“…suggested a deep learning-based technique mixed with semantic segmentation technology. Although Nugraha et al 15 . suggested using ML to precisely identify railroad irregularities and forecast the state of vital components.…”

Section: Previous Workmentioning

confidence: 99%

Employing a hybrid model based on texture-biased convolutional neural networks and edge-biased vision transformers for anomaly detection of signal bonds

2023

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A review of artiﬁcial intelligence applications in high-speed railway systems

Li,

Zhu,

Zhang

et al. 2024

High-speed Railway

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A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation

Consilvio,

Vignola,

López Arévalo

et al. 2024

Eur. Transp. Res. Rev.

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The application of artificial intelligence (AI) techniques may lead to significant improvements in different aspects of rail sector. Considering asset management and maintenance, AI can improve data analysis and asset status forecasting and decision-making processes, fostering predictive and prescriptive maintenance strategies. A prescriptive approach should be able to predict future scenarios as well as to suggest a course of actions. Nevertheless, the decision-making in rail asset management is often based on the classical asset-oriented approach, concentrating on the function of the asset itself as a main key performance indicator (KPI), whereas a user-oriented approach could lead to improved performance in terms of level of service. This paper is aimed at integrating the passengers’ perspective in the decision-making process for asset management to mitigate the impact that service interruptions may have on the final users. A data-driven prioritisation framework is developed to prioritise maintenance interventions taking into account asset status and criticality. In particular, a three-step approach is proposed, which focuses on the analysis of passenger data to evaluate the failure impact on the service, the analysis of alarms and anomalies to evaluate the asset status, and the suggestion of maintenance interventions. The proposed approach is applied to the maintenance of the metro line M5 in the Italian city of Milan. Results show the usefulness of the proposed approach to support infrastructure managers and maintenance operators in making decisions regarding the priority of maintenance activities, reducing the risk of critical failures and service interruptions.

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Detection of Railroad Anomalies using Machine Learning Approach

Cited by 3 publications

References 8 publications

Employing a hybrid model based on texture-biased convolutional neural networks and edge-biased vision transformers for anomaly detection of signal bonds

Employing a hybrid model based on texture-biased convolutional neural networks and edge-biased vision transformers for anomaly detection of signal bonds

A review of artiﬁcial intelligence applications in high-speed railway systems

A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation

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