Integrated methodology for correlating dynamic parameters with wheel wear progression in a scaled railway system

TL;DR

This paper introduces a new method to study how dynamic factors affect wheel wear in a scaled railway system, using synchronized data and imaging.

Contribution

The novelty lies in integrating multisensor data and microscopic imaging to create a correlative dataset for wheel wear analysis.

Findings

Synchronized data acquisition and imaging reveal how operational factors influence wheel wear.

A publicly available dataset supports predictive modeling and maintenance algorithm development.

Controlled experiments with varying speed and braking zones ensure reproducible results.

Abstract

Railway wheel wear poses major safety and maintenance challenges, yet accurate predictive models are limited by a lack of synchronized dynamic and wear data from scaled systems. This article presents an integrated methodology to generate a correlative dataset of dynamic parameters and wear progression on the wheels of a 1:20 scale railway system. The experimental approach combines synchronized multisensor data acquisition with sequential microscopic imaging under controlled operating conditions, specifically during braking maneuvers at track transitions. The resulting publicly available dataset enables direct analysis of how operational factors influence physical degradation.

Integration of synchronized sensor data with sequential microscopic imaging to correlate dynamics and wear progression.

Controlled factorial experimental design varying speed and braking zones to ensure reproducible testing conditions.

Publicly available dataset supporting model calibration, predictive algorithm development, and defect quantification for railway maintenance applications.

Image, graphical abstract

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