A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities

In this work, we propose a general modelling approach to estimate the life cycle cost of a system equipped with Prognostics and Health Management (PHM) capabilities, undergoing a Condition-Based Maintenance (CBM) policy. The approach builds on the Markov Chain theoretical framework, with transition probabilities linked to both PHM performance metrics of the literature and a novel metric. The developed approach can be used to guide economic decisions about CBM development, whichever the PHM algorithm is but provided that its performance metrics are estimated. The model is validated through a case study concerning a mechanical component of a train bogie affected by fatigue degradation, considering two different prognostic algorithms: Particle Filtering and a Model-Based approach.