CONDITION-BASED MAINTENANCE OPTIMISATION USING GENETIC ALGORITHM AND CONTINUOUS EVENT SIMULATION TECHNIQUE

Abstract

Effective maintenance strategies are of utmost significance for system engineering due to their direct linkage with financial aspects and safety of the plants’ operation. At a point where the state of a system, for instance, level of its deterioration, can be constantly observed, a strategy based on Condition-Based Maintenance (CBM) may be affected; wherein, upkeep of the system is done progressively on the premise of monitored state of the system. In this thesis, a multi-component framework is considered that is continuously kept under observation. In order to decide an optimal deterioration stage for the said system, Genetic Algorithm (GA) technique has been utilized that figures out when its preventive maintenance should be carried out. The system is configured into a multi-objective problem that is aimed at optimising the two desired objectives, namely profitability and accessibility. For the sake of reality, a prognostic model portraying the advancements of deteriorating system has been employed that will be based on utilization of continuous event simulation techniques. In this regard, Monte Carlo (MC) simulation has been shortlisted as it can take into account a wide range of probable options that can help in reducing uncertainty. The inherent benefits proffered by the said simulation technique are fully utilized to display various elements of a deteriorating system working under stressed environment. The proposed synergic model (GA & MC) is considered to be more effective due to the employment of ‘drop-by-drop approach’ that permits successful drive of the related search process with regards to the best optimal solutions.