Majid Mardani Shahri - Ph.D. student, department of industrial engineering, Sharif University of technology
Abdolhamid Eshraghniye Jahromi - Professor, department of industrial engineering, Sharif University of technology
Mahmoud Houshmand - Professor, department of industrial engineering, Sharif University of technology

The purpose of maintenance management is to maximize useful life, reliability, and efficiency of assets to efficiently utilize resources such as manpower, equipment facilities, and capital that are limited. Therefore, companies prefer to make use of such resources on critical equipment. To identify critical equipment, determining maintenance significant items (MSIs) has been recognized as one of the essential steps in reliability-centered maintenance (RCM) strategy. There is a lot of equipment in manufacturing companies; therefore, it is time-consuming and costly to determine their criticality. Accordingly, two novel intelligent models were proposed in this study to determine the criticality of system equipment and prioritize them within the RCM perspective. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the criticality of equipment in the system. Collecting real data from a gas refinery company and using them in the proposed models verified the applicability of such developed models. Moreover, the results showed that both ANN and ANFIS models could be used to predict criticality of equipment although the ANFIS model was endowed with better values for all performance indicators.

Reliability-Centered Maintenance (RCM), Soft Computing, Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference system (ANFIS), (MCDM), Gas Refinery