A Maintenance Cost Optimization Strategy Based on Prognostics and Health Monitoring Information

Abstract

Maintenance interventions impact the operational cost of equipment and systems. The use of Prognostics and Health Monitoring (PHM) information to support the implementation of an effective maintenance strategy has been a topic of great interest among researchers and industry practitioners. This paper presents a condition-based maintenance (CBM) optimization strategy for a single unit degrading system. The system under consideration is subjected to a degradation process that is modeled as a Gamma process. The system degradation level is obtained during non-periodic inspections. After each inspection, a maintenance scope is defined in order to minimize the expected maintenance cost per time unit. If a system failure is revealed during an inspection, the system is replaced and its degradation level is restored to the "as good as new" state. If the system is still functioning but with at a high degradation level, imperfect repair actions take place. Imperfect repair actions reduce the degradation level, but the system may still present a residual degradation level. In addition, imperfect repair actions may cause an increase in the system degradation rate. In the proposed strategy, the decision-maker may also decide not to perform maintenance after an inspection. The proposed maintenance strategy can dynamically define the best maintenance decision at each inspection, as well as the best time to perform the next inspection, based on system degradation level and the involved costs. Numerical experiments are carried out to illustrate the application of the proposed strategy. The impact of system degradation variability and inspection cost in the performance of the proposed strategy is analyzed.