Predictive maintenance is a key factor in improving the competitiveness of rail freight as it enables the transformation from traditional time-based maintenance to more efficient event-driven maintenance policies. In an optimal situation, wagons would be stopped for maintenance when needed and not at fixed intervals. To enable this dynamic method of managing maintenance, the faults developing on critical components would need to be detected at an early stage. If this could be achieved, the availability of the wagons would increase and the frequency of changing or repairing components would be reduced, and therefore the service life of components would increase, reducing the operating costs of the vehicle.
The objective of Work Stream 3 is to develop an integrated predictive maintenance approach to enable efficient use of both remote condition monitoring and historical data, and further support the implementation of predictive models and tools in rolling stock maintenance programmes.
The predictive maintenance scheme is based on four main components:
- identification of failure mechanisms;
- health monitoring of the system and detection of faults using sensor data;
- prognosis of the component, i.e., identification of the remaining useful life;
- maintenance decision based on rules and limit values.
Research activities in workstream 3 are organised along the following lines of action:
- Definition of Benchmark, Market Drivers and Specifications for predictive maintenance;
- Gathering, analysis and selection of historical data and data from condition monitoring systems, for subsequent use in the WP;
- Prioritisation of freight vehicle components and sub-systems in terms of their relevance for predictive maintenance;
- Analysis of failure data of freight vehicle components and sub-systems, and development of predictive models;
- Development of condition monitoring and fault detection schemes;
- Development of predictive maintenance procedures to improve the wagon’s maintenance process;
- Assessment of benefits for the predictive maintenance schemes developed in the WP and evaluation of the impact on the vehicle’s LCC, reliability and availability;
- Investigation of the potential integration of WS1, WS2 and WS3 outcomes in a single intelligent wagon concept.