Development of reliability-centered maintenance model using linguistic failure mode and effect analysis

The oil and gas industry is looking for techniques to accurately identify and prioritize the failure modes (FMs) of the equipment and precisely determine the proper maintenance policy and schedule for the failure prevention and reliability satisfaction. Failure mode and effect analysis (FMEA) is an essential tool used in the maintenance approach for the prevention of malfunctioning of the equipment. Moreover, Reliability Centered Maintenance (RCM) is a methodology to choose what maintenance activities have to be performed to keep the asset working within its designed function. Current developments in the FMEA technique are mainly focused on addressing the shortcomings of the conventional risk priority number calculations, but the group effects and interrelationships of FMs on other measurements are neglected. Likewise, current developments in the RCM models are struggling to solve the drawbacks of the traditional RCM, but prioritization of risk factors, multi-objective optimization, and policy selection of maintenance are neglected. Furthermore, maintenance reliability optimization for oil and gas industry is a hard task, which should balance inconsistent multi-objectives simultaneously, such as risk prioritization, cost and reliability optimization, and proper maintenance task selection. Hence, in the present study, a developed RCM model was proposed to fill these gaps and find the optimal maintenance policies and scheduling by a combination of multi-objective phases simultaneously. In the proposed RCM model, linguistic failure mode and effect analysis (LFMEA) was used to identify and prioritize the risk weight of FMs in the first section. Then, a co-evolutionary multi-objective particle swarm optimization (CMPSO) algorithm was applied to handle multi-objective optimization problems and to find the solution sets. An analytic network process (ANP) and a developed maintenance decision tree (DMDT) were used to find the optimal maintenance policy and schedule for every FMs. Moreover, the proposed LFMEA was proposed based on the combination of modified linguistic FMEA (LFMEA), ANP, and decision making trial and evaluation laboratory (DEMATEL) techniques. To validate the effectiveness and efficiencies of the proposed RCM model, a maintenance case study of the electrical gas turbine generator was conducted at Yemen oil and gas plant. Condition-based maintenance (CBM) was selected as the optimal policy for the highest risk priority of FMs which are associated with the hazard of gas and mechanical failures due to a substantial impact on production loss, safety, and machine reliability. After the proposed maintenance plan execution, the proposed RCM model have shown sufficient effectiveness in reliability and unavailability with real cost reduction, saved by 38.7% for the case study of electrical generator based on the application of the proposed RCM model. The experimental results have shown that the proposed model has a reasonable consideration for criteria weight, safety, production loss, and repair cost to carry out maintenance policy selection and scheduling. This could tap into the proposed model to evaluate and classify risk in order to prevent future failure and help decision-maker, especially in hazardous areas such as a nuclear and electrical gas plant. The results verify that, compared with the previous studies, the proposed model gave the optimal maintenance policies and scheduling in a well-structured plan economically and eff ectively. Finally, an RCM model was developed to overcome the RCM drawbacks. The proposed RCM model can assist decision-makers in choosing a suitable maintenance policy for machine to prevent possible failures and decrease their consequences. Moreover, identification of failure modes and their prioritization may be useful for particular risk analysis and judgments in complex or vague conditions, consequently increasing the success of reliability maintenance system.

Text FK 2020 7 - ir.pdf Download (1MB)

Actions (login required)

View Item