Maintenance planning for prognostic health management of multi-component systems: a case study of multi-objective optimisation for railway vehicles

Abstract

Condition-based maintenance (CBM) offers an opportunity to improve classical preventive maintenance strategies by transitioning to a dedicated prognostics & health management (PHM) strategy for individual components. This means that time-based fixed maintenance intervals are being replaced by condition-dependent and component-specific interventions.

For multi-component, complex systems, such a component-condition-based maintenance strategy yields multiple remaining useful life (RUL) prognostics. Maintainers must combine these into optimal maintenance plans that account for reliability, availability, maintainability, safety and costs (RAMS-C). Combining multiple RUL prognostics with the system's real-world dynamics, including architecture, anticipated future operating conditions, economic interdependence, stochastic interdependence and structural interdependence, yields complex maintenance planning decisions. To address this problem, several studies propose solutions based on multi-objective optimisation algorithms. However, these are often based on a priori knowledge and do not account for the day-to-day dynamics of operational variations. This paper presents a methodology for managing the complexity of maintenance scheduling under these conditions. The model is applied to the maintenance history of a Voith Maxima 30CC cargo locomotive. A priori maintenance models are compared with real-life implementation and optimised models using evolutionary multi-objective optimisation (EMO) to assess the rigour of the a priori plans under varying operating conditions. The study shows that applying such algorithms can significantly reduce costs while increasing overall system reliability and availability.