Data Driven Monitoring of Rolling Stock Components

In the rolling stock business, the digital age marks the arrival of a new paradigm for operation, maintenance and efficiency: combining data gathered from millions of machine and infrastructure sensors with big data analytics capabilities allows to monitor entire fleets down to individual components and plan maintenance actions only when they are necessary. This manuscript presents a case study of train-door condition monitoring in which a machine learning platform is leveraged to efficiently monitor and predict anomalies.