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Energy supply for high-speed trains is mainly attained witha high-voltage catenary (i.e., the source on the infrastructure)in contact with a sliding pantograph (i.e., the drain on therolling-stock vehicle). The friction between these two elementsis minimised with a carbon strip that the pantographequips. In addition to erosion, this carbon strip is also subjectto abrasion due to the high current that flows from the catenaryto the train. Therefore, it is of utmost importance to keepthe degradation of the carbon material under control to guaranteethe reliability of the railway service. To attain this goal,this article explores an accurate (i.e., uncertainty bounded)predictive method based on a robust online non-linear multivariateregression technique, considering some factors thatmay have an impact on the degradation on the carbon strip,such as the seasonal condition of the contact wire, which maydevelop an especially critical ice build-up in the winter. Theproposed approach uses a neural ensemble to integrate allthese sources of potential utility with the carbon strip data,which is convoluted in time with a set of spreading filters toincrease the overall robustness. Finally, the article evaluatesthe effectiveness of this prognosis approach with a dataset ofpantograph carbon thickness measurements over a year at thefleet level. The results of the analysis prove that it is definitelypossible to deploy a fine prediction, and thus yield a new avenuefor business improvement through the application of thepredictive maintenance approach to pantograph carbon strips.

Enhancing Railway Pantograph Carbon Strip Prognostics with Data Blending through a Time-Delay Neural Network Ensemble