Environment-aware maintenance planning: Performance assessment of adaptive condition-based maintenance strategies using Monte Carlo methods

In the context of modern industrial asset management, where operating conditions are characterized by abrupt and unpredictable fluctuations, conventional static maintenance policies often prove inadequate for ensuring both cost efficiency and system reliability. This study presents a comparative evaluation of two adaptive Condition-Based Maintenance (CBM) strategies designed for systems subject to stochastic degradation and non-stationary environmental dynamics. The first strategy, denoted as (ΔT k , M), dynamically adjusts inspection intervals according to the prevailing environmental state while maintaining a constant preventive replacement threshold. The second strategy, denoted as (ΔT, M k ), employs a fixed inspection interval and adapts the preventive replacement threshold in response to environmental stress. System degradation is modeled using a Wiener stochastic process, and environmental transitions are represented by a discrete-time Markov chain. A comprehensive economic model incorporating inspection, preventive replacement, corrective replacement, and downtime costs is formulated to assess long-run maintenance performance. Extensive Monte Carlo simulations are conducted to generate empirical distributions of maintenance outcomes and to evaluate the economic efficiency and robustness of both strategies. The results reveal a substantial performance advantage for the (ΔT k , M) strategy, which yields a mean total maintenance cost of 1687.5 units, compared with 8843.1 units for the (ΔT, M k ) strategy, corresponding to a reduction of approximately 81% in total maintenance cost. In addition to lowering average costs, the time-adaptive strategy significantly reduces corrective interventions and downtime while exhibiting greater robustness to stochastic variability. These findings demonstrate that integrating environmental information into inspection scheduling constitutes a more effective and economically advantageous adaptive mechanism than adjusting preventive thresholds alone.