A Dynamic Fuzzy Expert System Based on Maintenance Indicators for Service Type Selection of Machinery

Due to the multiplicity of standards and complex rules, maintenance, repair, and servicing of machinery could be done only by the fully qualified and proficient experts. since the knowledge of such experts is not accessible all times, using expert systems can help to improve the maintenance process. to address this need and the uncertainty of the maintenance process indicators, this research proposed a fuzzy expert systems (fes) for decision-making on the type of service. since not all indicators identified in the literature are important adequately, firstly, indicators that are more influential in the service type selection are chosen using inferential statistical analysis. then, these selected indicators designed the fuzzy rules of the knowledge based. finally, inference engine has been designed based on mamdani model to detect the service type of equipment. this research selected shemsh sazan zanjan company as a case study to implement the proposed expert system. according to our experiments, the proposed system increases the reliability by suggesting effective ideas that lead to decreasing production line breakdowns. the main contribution of this paper is providing a new approach to designing maintenance dynamic fes based on maintenance indicators for service-type selection that can decrease production line breakdowns.