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Machine learning-based parametric analysis of railway systems

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.

Otras comunicaciones del congreso o articulos relacionados con autores/as de la Universidad de Cantabria

 Congreso: International Conference on Railway Technology (6º : 2024 : Praga)

 Editorial: Civil-Comp Press

 Año de publicación: 2024

 Nº de páginas: 13

 Tipo de publicación: Comunicación a Congreso

 DOI: 10.4203/ccc.7.8.13

 ISSN: 1759-3433

 Proyecto español: PID2021-128031OB-I00