Operational Wheel Flat Detector in Railway Vehicles

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.