Publication

details

Using data from multiple wayside train monitoring systems to detect and estimate the size of wheel flats on railway vehicles

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Authors

Wolfgang Birk, Aron Laurell Håkansson, Henrik Lindström, Jesper Westerberg

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Published date

2025-05-15

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Venue/publisher

International Congress and Workshop on Industrial AI and eMaintenance

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Keywords

Wheel flats, wayside train monitoring systems, dynamic vehicle model, detection, length estimation

Summary

The aim of this paper is to propose a method for detecting the occurrence of wheel flats and estimating their size from data of multiple wayside train monitoring systems during normal operation. Thereby, the need for manual inspections can be reduced or at least becomes more efficient, releasing resources to focus on other aspects of maintenance. Moreover, decision making on wheel set exchanges and wagon shunting is improved and the risk for human errors is reduced.

The detection of wheel flats based on the fact that wheel flats usually occur on both wheels of a wheel set at the same time rendering a jump in the recorded rail-wheel contact forces by wheel impact load detectors. The wheel flat length estimation uses a model-based approach where simulated forces are matched with the measured forces by adjusting the flat length in the simulation. The scheme is evaluated on several re-life cases showing promising result and will be further validated in field tests.

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Citation

Birk, W., Laurell Håkansson, A., Lindström, H., Westerberg, J., (2025), Using Data from Multiple Wayside Train Monitoring Systems to Detect and Estimate the Size of Wheel Flats on Railway Vehicles, In Proceedings of the International Congress and Workshop on Industrial AI and eMaintenance, 13-15 May 2025, Luleå, Sweden.