Detecting wheel slip from railway operational data through a combined wavelet, long short-term memory and neural network classification method
| dc.contributor.author | Namoano, Bernadin | |
| dc.contributor.author | Emmanouilidis, Christos | |
| dc.contributor.author | Starr, Andrew | |
| dc.date.accessioned | 2024-09-18T10:33:21Z | |
| dc.date.available | 2024-09-18T10:33:21Z | |
| dc.date.freetoread | 2024-09-18 | |
| dc.date.issued | 2024-11 | |
| dc.date.pubOnline | 2024-08-30 | |
| dc.description.abstract | — Detecting wheel slip is important in railway operations, to prevent damage to wheels and tracks, reduce maintenance costs, improve safety and enhance passenger comfort. Slip activity is characterised by reduced adhesion between the wheel and the rail and limits effective braking or acceleration, causing also operational risks. It is influenced by environmental conditions, vehicle load, track and axle quality, contaminants, inclines, rail oxidation, and braking forces. This paper introduces an innovative method for wheel slip detection in operational trains, utilizing wavelet analysis combined with Long-Short Term Memory (LSTM) modelling. This method analyzes operational data to effectively identify wheel slip, showing promising results when compared to traditional classification-based machine learning methods such as decision trees, forests, logistic regression, naïve Bayes, and support vector machines. This novel approach addresses the complexities of wheel slip detection and is capable of identifying the conditions leading to slip several seconds prior to the commencement of the slip event, offering a practical solution for real-world railway systems. | |
| dc.description.journalName | Engineering Applications of Artificial Intelligence | |
| dc.description.sponsorship | The authors wish to thank Unipart Rail, Instrumentel for providing the industrial data and domain-specific expertise and the Engineering and Physical Sciences Research Council (EPSRC) for the financial support through grant 2203091 | |
| dc.identifier.citation | Namoano B, Emmanouilidis C, Starr A. (2024) Detecting wheel slip from railway operational data through a combined wavelet, long short-term memory and neural network classification method. Engineering Applications of Artificial Intelligence, Volume 137, Part B, November 2024, Article number 109173 | |
| dc.identifier.elementsID | 552549 | |
| dc.identifier.issn | 0952-1976 | |
| dc.identifier.paperNo | 109173 | |
| dc.identifier.uri | https://doi.org/10.1016/j.engappai.2024.109173 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/22944 | |
| dc.identifier.volumeNo | 137 | |
| dc.language | English | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.publisher.uri | https://www.sciencedirect.com/science/article/pii/S0952197624013319?via%3Dihub | |
| dc.rights | Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | 46 Information and Computing Sciences | |
| dc.subject | 40 Engineering | |
| dc.subject | Artificial Intelligence & Image Processing | |
| dc.subject | 40 Engineering | |
| dc.subject | 46 Information and computing sciences | |
| dc.subject | Anomaly detection | |
| dc.subject | Train wheel slip | |
| dc.subject | Railway operations | |
| dc.subject | Wavelet analysis | |
| dc.subject | Long short term memory | |
| dc.subject | Classification | |
| dc.title | Detecting wheel slip from railway operational data through a combined wavelet, long short-term memory and neural network classification method | |
| dc.type | Article | |
| dc.type.subtype | Journal Article | |
| dcterms.dateAccepted | 2024-08-19 |