An Integrated Risk-Informed Multicriteria Approach for Determining Optimal Inspection Periods for Protective Sensors.

Equipment failure is the leading cause of industrial operational disruption, with unplanned downtime accounting for up to 11% of manufacturing revenue, highlighting the need for effective proactive maintenance strategies, such as protective sensors that can detect potential failures in critical equipment before a functional failure occurs. However, sensors are also subject to hidden failures themselves, requiring periodic failure-finding inspections. This study proposes a novel integrated multimethodological approach combining discrete event simulation, Monte Carlo, optimization, risk analysis, and multicriteria decision analysis methods to determine the optimal inspection period for protective sensors subject to hidden failures. Unlike traditional single-objective models, this approach evaluates alternative inspection periods based on their risk-informed overall values, considering multiple conflicting key performance indicators, such as maintenance costs and equipment availability. The optimal inspection period is then selected considering uncertainties and the intertemporal, intra-criterion, and inter-criteria preferences of the organization. The approach is demonstrated through a case study at the leading Portuguese electric utility, replacing previous empirical inspection standards that did not consider economic costs and uncertainties, supported by an open, transparent, auditable, and user-friendly decision support system implemented in Microsoft Excel using only built-in functions and modeled based on the principles of probability management. The results identified an optimal inspection period of 90 h, representing a risk-informed compromise distinct from the 120 h interval suggested by cost minimization alone, highlighting the importance of integrating organizational preferences into the decision process. A sensitivity analysis confirmed the robustness of this solution, maintaining validity even as the organizational weight for equipment availability ranged between 35% and 82%. The case study shows that the proposed approach enables the identification of inspection intervals that lead to quantitatively better maintenance cost and availability outcomes compared to empirical inspection standards.