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Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Najeh, Taoufik; Lundberg, Jan; Kerrouche, Abdelfateh


Taoufik Najeh

Jan Lundberg


The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.


Najeh, T., Lundberg, J., & Kerrouche, A. (2021). Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings. Sensors, 21(15), Article 5217.

Journal Article Type Article
Acceptance Date Jul 29, 2021
Online Publication Date Jul 31, 2021
Publication Date 2021
Deposit Date Aug 24, 2021
Publicly Available Date Aug 24, 2021
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 21
Issue 15
Article Number 5217
Keywords switches and crossings; wear measurement; deep learning; LSTM; ResNet vibration sensors
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Deep-Learning And Vibration-Based System For Wear Size Estimation Of Railway Switches And Crossings (5.7 Mb)

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Copyright Statement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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