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Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-4895-5300
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-7744-2155
School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK.
2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 15, article id 5217Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 21, no 15, article id 5217
Keywords [en]
switches and crossings, wear measurement, deep learning, LSTM, ResNet vibration sensors
National Category
Infrastructure Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-86551DOI: 10.3390/s21155217ISI: 000682269200001PubMedID: 34372454Scopus ID: 2-s2.0-85111439469OAI: oai:DiVA.org:ltu-86551DiVA, id: diva2:1584225
Note

Validerad;2021;Nivå 2;2021-08-16 (alebob)

Available from: 2021-08-11 Created: 2021-08-11 Last updated: 2022-02-10Bibliographically approved

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Najeh, TaoufikLundberg, Jan

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