Abstract

The rail fastening system plays a crucial role in railway tracks as it ensures operational safety by fixing the rail on to the sleeper. Early detection of rail fastener system defects is crucial to ensure track safety and to enable maintenance optimization. Fastener inspections are normally conducted either manually by trained maintenance personnel or by using automated 2-D visual inspection methods. Such methods have drawbacks when visibility is limited, and they are also found to be expensive in terms of system maintenance cost and track possession time. In a previous study, the authors proposed a train-based differential eddy current sensor system based on the principle of electromagnetic induction for fastener inspection that could overcome the challenges mentioned above. The detection in the previous study was carried out with the aid of a supervised machine learning algorithm. This study reports the finding of a case study, along a heavy haul line in the north of Sweden, using the same eddy current sensor system mounted on an in-service freight train. In this study, unsupervised machine learning models for detecting and analyzing missing clamps in a fastener system were developed. The differential eddy current measurement system was set to use a driving field frequency of 27 kHz. An anomaly detection model combining isolation forest (IF) and connectivity-based outlier factor (COF) was implemented to detect anomalies from fastener inspection measurements. To group the anomalies into meaningful clusters and to detect missing clamps within the fastening system, an unsupervised clustering based on the DBSCAN algorithm was also implemented. The models were verified by measuring a section of the track for which the track conditions were known. The proposed anomaly detection model had a detection accuracy of 96.79% and also exhibited a high score of sensitivity and specificity. The DBSCAN model was successful in clustering missing clamps, both one and two missing clamps, from a fastening system separately.

Highlights

  • Railway transportation is a significant mode of transportation for reasons of environmental friendliness, safety, cost, and lower energy consumptions

  • In previous studies [6,29], the authors proposed an alternate approach using a trainIndifferential previous studies the sensor authorsfor proposed aninspection alternate approach a train‐major based eddy[6,29], current fastener that canusing overcome based differential eddy current sensor for fastener inspection that can overcome major an challenges associated with automated visual inspection systems

  • This paper presents challenges associated with automated visual inspection systems

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Summary

Introduction

Railway transportation is a significant mode of transportation for reasons of environmental friendliness, safety, cost, and lower energy consumptions. It is a sustainable mode of transportation that supports the economic and industrial expansion of the society through the mobilization of freight and passengers [1]. The growing demand to shift huge volumes of passengers and freight traffic and the current state of the existing railway infrastructure are issues that require substantial attention in the field of transportation [2]. Capital expansion of the railway infrastructure is a cost-intensive and time-consuming approach. The maintenance and renewal (M&R) process needs to be subjected to continuous improvement for the existing infrastructure to meet the capacity demand without compromising the quality of the provided service [3,4]. 4.0/).

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