Designing CBM Plans, Based on Predictive Analytics and Big Data Tools, for Train Wheel Bearings

Abstract

Modern train fleets have very demanding requirements in passenger safety, train service reliability and availability, comfort and life cycle costs. To reach these goals, maintenance intervals of more than thirty thousand kilometers besides serious failure-free objectives exceeding one and a half million kilometers are becoming a standard. This requires manufacturers to develop bold designs and to use advanced engineering tools for the Operations and Maintenance (O&M) of such trains. Condition Based Maintenance (CBM) solutions, using condition monitoring systems and advanced algorithms to detect commencing deterioration, may allow sufficient time for maintenance before serious failures can develop, which increases safety, reliability and availability while helping to reduce operating and maintenance expenses and the total cost of ownership.This paper applies predictive analytics, big data processes and tools to design CBM Plans for train axle bearings, to increase both preventive maintenance (PM) intervals and dependability of the trains. The paper details how the machine learning predictive model is selected and how the model is trained with different data sets. Big data processes allow to test and accept a universal model per bearing position regardless the axle or train of the fleet, overcoming complexity that could be generated by the non-ergodicity of these assets. The originality of this work consists in the ability to identify bearing deterioration related anomalies, by an innovative modeling and prediction of axle bearing temperature using data analytics. Also, interpretation rules for early failure detection based on these advanced predictive analytics are compared to those already existing rules in the train on-board control monitoring system (TCMS) ensuring train’s safety. Conclusions of the work are related to the process followed and the validity of results.