System Maintenance Scheduling With Prognostics Information Using Genetic Algorithm
Condition Based Maintenance (CBM) aims to balance two extreme sides (i.e., Corrective Maintenance ( CM), and Preventive Maintenance ( PM)) by observing and forecasting the real time health of machines. Recent developments in CBM revealed promising technologies for advanced fault detection, and forecasting. Traditional maintenance scheduling in CBM is based on the threshold setting on forecasted failure probability, or remaining useful life (RUL) for individual components. However, this approach may not give the best result for the system, because individual components are inter-related, and mutually dependent. It is not uncommon in systems that turning off a machine due to failure or maintenance causes other machinery or components to be turned off. Designing a comprehensive tool that optimizes availability & cost of the whole system incorporating prognostics information is crucial to fully benefit from CBM. The goal of this paper is to emphasize this need by demonstrating scenarios in CM, PM, and CBM; and to present a solution that optimizes system availability, and cost with system-maintenance constraints using genetic algorithms. The proposed tool acquires the forecasted failure probability of individual components from the prognostics module, and their reliability expectations after maintenance. The tradeoff between maintenance & failure is quantified in risk as the objective function to be minimized. The risk is minimized utilizing genetic algorithms for the whole system rather than individual components. The results of the proposed tool are compared with PM, CM, and CBM in which prognostics information of components are analyzed individually.
maintenance scheduling prognostics genetic algorithms condition based maintenance remaining useful life maintenance optimization maintenance planning