Robust sensors enabling condition-based maintenance of lubricated components in locomotives and wagons

Abstract

Digitalization in mobility is considered the key to success to increase efficiency, reliability and safety in rail transport, both track and rolling stock. “Big data” analytics are therefore implemented. In the European Joint Undertaking “Shift2Rail”, tremendous efforts are dedicated to condition-based maintenance (CBM). The task of AC2T research GmbH is to enable data collection, processing and evaluation by appropriate sensor systems for online health status monitoring of lubricated components in locomotives and wagons to increase safety and availability while reducing of maintenance costs and unplanned downtime. Special attention is paid to robustness of both sensor and algorithm to meet the high demands for reliability in rail transport. Suitable sensor systems were compiled for three use cases at locomotives and wagons by the field-to-lab approach and validated for use in CBM.