Evolution of Rail Contact Fatigue on Crossing Nose Rail Based on Long Short-Term Memory

Lei Kou, Mykola Sysyn (), Jianxing Liu, Olga Nabochenko, Yue Han, Dai Peng and Szabolcs Fischer ()
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Lei Kou: Institute of Railway Systems and Public Transport, Technical University Dresden, 01069 Dresden, Germany
Mykola Sysyn: Institute of Railway Systems and Public Transport, Technical University Dresden, 01069 Dresden, Germany
Jianxing Liu: School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
Olga Nabochenko: Institute of Railway Systems and Public Transport, Technical University Dresden, 01069 Dresden, Germany
Yue Han: China Railway Hohhot Group Co., Ltd., Hohhot 012000, China
Dai Peng: Infrastructure Inspection Research Institute, China Academy of Railway Sciences, Beijing 100081, China
Szabolcs Fischer: Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil and Transport Engineering, Szechenyi Istvan University, 9026 Gyor, Hungary

Sustainability, 2022, vol. 14, issue 24, 1-17

Abstract: The share of rail transport in world transport continues to rise. As the number of trains increases, so does the load on the railway. The rails are in direct contact with the loaded wheels. Therefore, it is more easily damaged. In recent years, domestic and foreign scholars have conducted in-depth research on railway damage detection. As the weakest part of the track system, switches are more prone to damage. Assessing and predicting rail surface damage can improve the safety of rail operations and allow for proper planning and maintenance to reduce capital expenditure and increase operational efficiency. Under the premise that functional safety is paramount, predicting the service life of rails, especially turnouts, can significantly reduce costs and ensure the safety of railway transportation. This paper understands the evolution of contact fatigue on crossing noses through long-term observation and sampling of crossing noses in turnouts. The authors get images from new to damaged. After image preprocessing, MPI (Magnetic Particle Imaging) is divided into blocks containing local crack information. The obtained local texture information is used for regression prediction using machine-supervised learning and LSTM network (Long Short-Term Memory) methods. Finally, a technique capable of thoroughly evaluating the wear process of crossing noses is proposed.

Keywords: contact fatigue; neural convolution; machine learning; turnout; switch; frog rail; rail surface; long short-term memory (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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