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

摘要

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.