Year: 2025
Type: article
Abstract: Predictive maintenance of railroads is essential to prevent costly disruptions. A critical aspect of this maintenance is ensuring the integrity of the pantograph-catenary system, where copper alloy wires experience continuous friction and wear. The degradation rate and condition of these wires are vital factors in planning maintenance activities. Current wear rate prediction methods are largely theoretical and often inaccurate, overlooking essential contextual details. Additionally, wire condition data frequently show inaccuracies and inconsistencies in spatial and temporal resolution, complicating the feasibility of using data-driven approaches. This research investigates a data-driven framework to accurately predict wear rates, emphasizing data processing and optimized data use. A dataset spanning nine years of Dutch railway infrastructure measurements is used, employing various machine learning techniques to determine the most effective approach. Findings indicate that, in 95% of cases, average wire thickness can be predicted with a precision of ±0.12 mm over a four-year period. This study advances the field by proposing a framework that addresses measurement errors, a common challenge in sensor-based assessments, making data-driven maintenance a more reliable option.
Source: Applied Sciences
DOI: 10.3390/app15179362
Authors: Jeroen Mulder, Faridaddin Vahdatikhaki, Xianfei Yin, Frank Vermeulen, Hans Voordijk
Institutions: University of Twente, City University of Hong Kong, ProRail
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4413682947