Balancing Trade-Offs in Condition-Based Maintenance with a Discrete-Time Markov Chain Model

This paper proposes a novel condition-based maintenance (CBM) model based on a discrete-time Markov chain. The model considers the system's degradation evolution over time and optimizes the preventive maintenance threshold and the time between inspections by minimizing the annual maintenance cost subject to a lower limit on the system's availability. The proposed approach considers various cost factors such as inspection costs, preventive maintenance costs, corrective maintenance costs, and downtime costs. The interior-point optimization method is used for the constrained nonlinear model. The model is applied to a case study from the process industry, and a cost and variability sensitivity analysis is performed. According to the findings, the proposed model effectively balances the trade-off between maintenance costs and system availability. In addition, the sensitivity analysis highlights the importance of accurate cost estimation and the role of key variables like inspection frequency and preventive maintenance threshold in determining the optimal maintenance policy.