Estimating the Uncertainty of Brake Pad Prognostics for High-speed Rail with a Neural Network Feature Ensemble

Abstract

The friction brake system reduces the speed of the train by transforming the kinematic energy into heat through the abrasion between the carbon pads and the disk. The British Rail Class 390 fleet (Pendolino) features a very high availability, running 1000 miles a day on average, so their wear rate is monotonic and acceptably constant. The prognostics for brake pad degradation are typically conducted with a robust online linear regression technique, which seamlessly accommodates asset-based idiosyncrasies, like the different effort that is exerted on the pad given its location on a motor or a trailer car, on the left or the right hand side of the caliper, etc. This technique is also resilient to abrupt measurement changes due to asset replacements, sensor imprecision, and acquisition failures, while retaining the physical evolution of the wear, which erodes the surface of the pad. This article evaluates the effectiveness of this approach with a dataset of brake pad thickness measurements, at the fleet level (around 12000 asset instances), using a sliding window technique, and refines its performance with a neural network ensemble, which blends physical and location features. The results of the analysis prove that this method meets the requirements of the maintenance staff and thus yields a new avenue for business improvement through the application of the predictive maintenance approach for brake pads.