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J. Magalhães¹, T. Jorge¹, A. Meixedo², A. Guedes², R. Silva² and D. Ribeiro¹

¹CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal
²CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal

doi:10.4203/ccc.7.8.12

J. Magalhães, T. Jorge, A. Meixedo, A. Guedes, R. Silva, D. Ribeiro, "Machine Learning Methodology for Identification of Multiple Out-of-Round Railway Wheels using Data from Wayside Monitoring Systems", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 8.12, 2024, doi:10.4203/ccc.7.8.12

Keywords: out-of-round (OOR) railway wheels, wayside monitoring system, damage detection, damage localization, damage type, multi-damage, machine learning, relative wavelet energy.

Abstract

This research presents a new automated diagnosis methodology for out-of-round multi-damage wheels that addresses the damage detection and localization, using only acceleration and strain data measured on the railway track. The methodology is based on wavelet relative energy and comprises two stages: i) detect damage through the wavelet entropy derived from vertical acceleration responses and ii) localize damage by mathematically processing wavelet decomposition and using strain responses to determine the specific axle location of the detected damaged wheel. The proposed methodology is numerically validated for two different types of out-of-round damage in railway vehicles, such as polygonal wheels and wheel flats, and for a five-car freight train with different damage combinations and localizations.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 8.12 Machine Learning Methodology for Identification of Multiple Out-of-Round Railway Wheels using Data from Wayside Monitoring Systems J. Magalhães¹, T. Jorge¹, A. Meixedo², A. Guedes², R. Silva² and D. Ribeiro¹ ¹CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal ²CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal doi:10.4203/ccc.7.8.12 Full Bibliographic Reference for this paper J. Magalhães, T. Jorge, A. Meixedo, A. Guedes, R. Silva, D. Ribeiro, "Machine Learning Methodology for Identification of Multiple Out-of-Round Railway Wheels using Data from Wayside Monitoring Systems", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 8.12, 2024, doi:10.4203/ccc.7.8.12 Keywords: out-of-round (OOR) railway wheels, wayside monitoring system, damage detection, damage localization, damage type, multi-damage, machine learning, relative wavelet energy. Abstract This research presents a new automated diagnosis methodology for out-of-round multi-damage wheels that addresses the damage detection and localization, using only acceleration and strain data measured on the railway track. The methodology is based on wavelet relative energy and comprises two stages: i) detect damage through the wavelet entropy derived from vertical acceleration responses and ii) localize damage by mathematically processing wavelet decomposition and using strain responses to determine the specific axle location of the detected damaged wheel. The proposed methodology is numerically validated for two different types of out-of-round damage in railway vehicles, such as polygonal wheels and wheel flats, and for a five-car freight train with different damage combinations and localizations. download the full-text of this paper (PDF, 1491 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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