Article: Fault prediction of railway track circuit based on machine learning Journal: International Journal of Sensor Networks (IJSNET) 2024 Vol.45 No.4 pp.216 - 228 Abstract: Railway track circuit designs are crucial and complicated for handling movement, halting, and swapping locomotives, goods, and passengers. The circuit has intelligently automated electro-mechanical and electronic equipment to ensure locomotive and passenger safety. The unattended or prolonged failure or faults in such circuits lead to disasters; precautionary measures are hence mandatory. This article introduces a precision fault detection technique using machine learning (PFDT-ML) for equipment-specific diagnosis. The data are taken from the Railway Track Fault Detection Kaggle dataset for analysing the defective and non-defective railway tracks. This technique studies the operation synchronisation between the control and monitoring devices and their prompt functions regularly. The role of transfer learning is to retain the operational and fault states of the equipment based on their synchronisation. This learning performs function transfer for state maintenance from synchronisation to monitoring. Therefore, synchronisation alerts the faulty states to the appropriate stations for early diagnosis. On the other hand, faulty or non-functional equipment is reported before the next synchronisation interval for appropriate precautions. Thus, the learning states are swapped recurrently until the operational state of the circuit equipment is restored for synchronisation. The experimental outcome demonstrates that the recommended PFDT-ML model increases the classification accuracy ratio of 98.9%, railway track zone detection rate of 97.5%, fault prediction ratio of 96.3%, and F1-score ratio of 95.6% compared to other popular models. Inderscience Publishers - linking academia, business and industry through research

Title: Fault prediction of railway track circuit based on machine learning

Authors: Xin Zhang; Yan Ru

Addresses: School of Urban Rail Transit, Liuzhou Railway Vocational Technical College, Liuzhou, Guangxi 545616, China ' School of Intelligent Manufacturing, Liuzhou Railway Vocational Technical College, Liuzhou, Guangxi 545616, China

Abstract: Railway track circuit designs are crucial and complicated for handling movement, halting, and swapping locomotives, goods, and passengers. The circuit has intelligently automated electro-mechanical and electronic equipment to ensure locomotive and passenger safety. The unattended or prolonged failure or faults in such circuits lead to disasters; precautionary measures are hence mandatory. This article introduces a precision fault detection technique using machine learning (PFDT-ML) for equipment-specific diagnosis. The data are taken from the Railway Track Fault Detection Kaggle dataset for analysing the defective and non-defective railway tracks. This technique studies the operation synchronisation between the control and monitoring devices and their prompt functions regularly. The role of transfer learning is to retain the operational and fault states of the equipment based on their synchronisation. This learning performs function transfer for state maintenance from synchronisation to monitoring. Therefore, synchronisation alerts the faulty states to the appropriate stations for early diagnosis. On the other hand, faulty or non-functional equipment is reported before the next synchronisation interval for appropriate precautions. Thus, the learning states are swapped recurrently until the operational state of the circuit equipment is restored for synchronisation. The experimental outcome demonstrates that the recommended PFDT-ML model increases the classification accuracy ratio of 98.9%, railway track zone detection rate of 97.5%, fault prediction ratio of 96.3%, and F1-score ratio of 95.6% compared to other popular models.

Keywords: fault prediction; railway operation; track circuit; transfer learning.

DOI: 10.1504/IJSNET.2024.140393

International Journal of Sensor Networks, 2024 Vol.45 No.4, pp.216 - 228

Received: 28 Apr 2024
Accepted: 11 May 2024
Published online: 06 Aug 2024 *

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Title: Fault prediction of railway track circuit based on machine learning

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