Wheel Damage Prediction Using Wayside Detector Data for a Cross-Border Operating Fleet with Irregular Detector Passage Patterns

Wheel damages on railway vehicles caused by rolling contact fatigue or blocked wheels can cause severe problems for railway operators and infrastructure owners. Wheel impact load wayside detectors (WILD) are one of the means to assess the condition of a wheel in operation, but varying operating routes, irregular traffic patterns, and especially cross-border operations make this quite challenging. While the condition updates occur randomly, the detectors themselves are managed by different owners and principles. Thus, using the same type of data from not only different wayside locations but also different providers and authorities with varying fidelity and operational practices introduces uncertainties in data quality and consistency. This paper presents an approach for predicting wheel damage severity on a wagon fleet with irregular cross-border operations, achieving similar confidence levels as for regular traffic patterns on a national scale. The different sensor characteristics are explored between countries and within each country. The approach is implemented as a cloud-based solution which integrates wayside detector data from multiple locations provided by two different infrastructure owners in two countries. The solution estimates remaining useful life based on data from both countries and aggregates this to a single indication for the decision maker. The algorithm’s performance is showcased for vehicles with cross-border operations. The results indicate that the proposed approach confirms that irregularly provided measurement data with data quality and consistency issues are manageable and adequate decision-making performance.