In the Industry 4.0 era, effective maintenance management is paramount to ensuring production continuity, operational efficiency, and cost-effectiveness. Modern industrial systems operate under inherent uncertainty and limited observability, necessitating the development of sophisticated decision-support frameworks. This study introduces a comprehensive approach to optimizing maintenance control for industrial assets under stochastic degradation and partial observability. The framework integrates stochastic processes for degradation modeling with Overall Equipment Effectiveness (OEE) and Life Cycle Cost (LCC) analysis for multi-dimensional performance assessment. Maintenance interventions are governed by threshold-based strategies, where optimal service limits (Θ*) are determined through extensive Monte Carlo simulations. Furthermore, both local and global sensitivity analyses are employed to identify critical drivers of decision-making, such as failure penalties, process volatility, and maintenance efficacy. The model is extended to incorporate Digital Twin concepts, enhancing state estimation under noisy sensor data, and addresses multi-machine scenarios with resource constraints to reflect real-world operational complexities. Results indicate that failure costs and process uncertainty are the primary determinants of maintenance timing. Notably, Digital Twin integration significantly bolsters decision accuracy in the presence of measurement noise, providing a robust and scalable solution for modern manufacturing environments.
Drożyner et al. (Thu,) studied this question.