Wayside Railway Switch and Crossing Monitoring Using Isolation Forest Anomaly Scores

Yang Zuo (), Praneeth Chandran, Johan Odelius and Matti Rantatalo
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Yang Zuo: Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, Sweden
Praneeth Chandran: Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, Sweden
Johan Odelius: Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, Sweden
Matti Rantatalo: Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, Sweden

Sustainability, 2023, vol. 15, issue 20, 1-14

Abstract: Railway switch and crossing (S&C) systems have complicated moving structures compared with regular rail. They require multiple components that vary in complexity. The complexity of railway S&C, together with the fact that they are discontinuous points of the system, makes them vulnerable to defects such as squats. A squat on the switching rail could potentially cause rail breakage and lead to catastrophic results, such as derailment. In this study, a method based on anomaly scoring was investigated to estimate the status of an S&C system with respect to squat defects. The proposed method was tested in a real environment under controlled measurement sequences. The results show that the methods can differ between an S&C with squats and another one without them.

Keywords: railway; anomaly detection; anomaly score; rail squat; squat detection; machine learning; unsupervised learning; isolation forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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