Optimizing Maintenance Strategies for Aircraft Repairable Units with Hidden Functions under Periodic Inspection and Imperfect Restoration

The creation of optimal maintenance plans for repairable units with hidden functions poses a significant challenge during the development of aircraft-maintenance programs. This challenge stems from the complexity introduced by the need to simultaneously consider various factors, including the availability of hidden functions during operation, the varying effectiveness of restoration and repair effectiveness, degradation due to ageing and usage, inspection quality, the impacts of safety measures, and the operational and economic consequences of failure. This paper introduces a decision support model to identify the optimal maintenance plan for a repairable unit with hidden functions, one that considers a combination of inspections, restorations, and a discard task at the unit’s predefined renewal age. This model considers the “non-safety effect” failure category (e.g., operational disruptions or delays, higher maintenance costs, secondary damage to equipment). It incorporates the Kijima type II model to account for the effect of imperfect restoration and the concept of mean fractional dead time to identify the optimal intervals for failure-finding inspections (FFIs), number of inspections needed within each restoration cycle, and frequency of restorations in a renewal cycle. The proposed model also considers the costs associated with inspection, repair, restoration, and accidents stemming from multiple-failures. For a clearer understanding and application of the model, we employ real-world adapted numerical examples to elucidate the proposed optimization model and facilitate the exploration of maintenance strategies in the absence of comprehensive real-world aviation data. This approach serves as a crucial tool for initial model validation and lays the groundwork for future empirical research. In addition, a sensitivity analysis is also performed to evaluate the effects of changes in reliability and cost parameters and determine the parameters that most strongly affect the maintenance-optimization results. Numerical examples are particularly suited for such analyses, as they allow for controlled manipulation of parameters to observe their impacts on the model's outputs. The analysis results indicate that, in the absence of an in-unit ageing process, restoration effectiveness has a profound effect on the total cost and optimal set of solutions. In a maintenance plan with ABAO restoration effectiveness, the optimal alternative involves maximizing the number of FFIs without resorting to restoration. Conversely, in a maintenance plan with AGAN restoration effectiveness, the optimal alternative should harmonize a moderate number of inspections with a moderate number of restorations. Additionally, the influence of the costs associated with restoration and accidents suggests that frequent inspections at smaller intervals are essential to mitigate the risk of failure. Maintenance managers can leverage the proposed model to compare and select maintenance strategies that are tailored to different effectiveness and cost parameters. This model constitutes a practical tool for maintenance optimization and provides valuable insights for decision-makers looking to enhance the reliability of repairable systems with hidden failure points and compare the cost-effectiveness of maintenance plans.