Operational Wheel Flat Detector in Railway Vehicles
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Abstract
Maintenance of railway systems is shifting from being based on scheduled interventions to a continuous regime based on the actual status of assets. This change is supported mainly on three pillars: the development of new sensors and signal processing techniques, the capability to store and analyze all the information gathered by this huge amount of new sensors, and the capability of modifying dynamically the maintenance plans. This paper presents a new wayside system for detecting flats whose development has been based on combining physical models with Machine Learning Techniques. Physical models are used to understand the phenomena, define the key indicators to characterize the phenomena and generate synthetic data to train Machine Learning algorithms. Subsequently, regression models are generated to relate the key parameters with the flat severity. The last part of the paper is focused on validating the proposed methodology in a real environment.
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Wheel Maitenance, Machine Learning, Physical Modelling
Wheel Defect Signal from Wheel–Rail Contact Signals Measured by Multiple Wayside Sensors». Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 233(1): 49-62.
Blanco, B. et al. 2019. «Implementation of Timoshenko Element Local Deflection for Vertical Track Modelling». Vehicle System Dynamics 57(10): 1421-44.
Blanco, B., N. Gil-Negrete, L. Kari, and A. Alonso. 2022. «On the Correction of Rail Accelerations Predicted by Numerical Track Models Based on Timoshenko Beam Theory». Vehicle System Dynamics 60(6): 1993-2017.
Gao, Run, Qixin He, Qibo Feng, and Jianying Cui. 2020. «In-Service Detection and Quantification of Railway Wheel Flat by the Reflective Optical Position Sensor». Sensors 20(17): 4969.
Alemi, Alireza, Francesco Corman, Yusong Pang, and Gabriel Lodewijks. 2019. «Reconstruction of an Informative Railway Wheel Defect Signal from Wheel–Rail Contact Signals Measured by Multiple Wayside Sensors». Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 233(1): 49-62.
Blanco, B. et al. 2019. «Implementation of Timoshenko Element Local Deflection for Vertical Track Modelling». Vehicle System Dynamics 57(10): 1421-44.
Blanco, B., N. Gil-Negrete, L. Kari, and A. Alonso. 2022. «On the Correction of Rail Accelerations Predicted by Numerical Track Models Based on Timoshenko Beam Theory». Vehicle System Dynamics 60(6): 1993-2017.
Gao, Run, Qixin He, Qibo Feng, and Jianying Cui. 2020. «In-Service Detection and Quantification of Railway Wheel Flat by the Reflective Optical Position Sensor». Sensors 20(17): 4969.
Iwnicki, Simon, Jens C. O. Nielsen, and Gongquan Tao. 2023. «Out-of-Round Railway Wheels and Polygonisation». Vehicle System Dynamics 61(7): 1785-1828.
Jergéus, J. 1998. «Martensite Formation and Residual Stresses around Railway Wheel Flats». Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 212(1): 69-79.
LBFoster. «WILD IV Product Brochure». https://m6g3q6n5.stackpathcdn.com/perch/resources/wild-mk-iv-product-brochure.pdf (3 de julio de 2023).
Maglio, Michele, Astrid Pieringer, Jens C.O. Nielsen, and Tore Vernersson. 2021. «Wheel–Rail Impact Loads and Axle Bending Stress Simulated for Generic Distributions and Shapes of Discrete Wheel Tread Damage». Journal of Sound and Vibration 502: 116085.
Mazilu, Traian. 2007. «Green’s Functions for Analysis of Dynamic Response of Wheel/Rail to Vertical Excitation». Journal of Sound and Vibration 306(1-2): 31-58.
Mosleh, Araliya et al. 2023. «Early Wheel Flat Detection: An Automatic Data-Driven Wavelet-Based Approach for Railways». Vehicle System Dynamics 61(6): 1644-73.
Mosleh, Araliya, Pedro Montenegro, Pedro Alves Costa, and Rui Calçada. 2021. «An Approach for Wheel Flat Detection of Railway Train Wheels Using Envelope Spectrum Analysis». Structure and Infrastructure Engineering 17(12): 1710-29.
Nielsen, JCO, JW Ringsberg, and L Baeza. 2005. «Influence of railway wheel flat impact on crack growth in rails». En Proceedings of the Eighth International Heavy Haul Conference (IHHC8) in Rio de Janeiro,.
Schenck process. «MULTIRAIL Improving safety and reliability in rail processes». https://www.schenckprocess.com/download?id=139&lang=en&pid=123 (3 de julio de 2023).
Stratman, Brant, Yongming Liu, and Sankaran Mahadevan. 2007. «Structural Health Monitoring of Railroad Wheels Using Wheel Impact Load Detectors». Journal of Failure Analysis and Prevention 7(3): 218-25.
voestalpine. «WDD/WIM wheel impact load detection». https://cdnstorevoestalpine.blob.core.windows.net/documents/792615/original/railwaysystems_factsheet_WIM-WDD_en.pdf (3 de julio de 2023).
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