An intelligent reliability centered maintenance analysis system based on case-based reasoning & rule-based reasoning

To aid RCM analysts in conducting efficient RCM analysis on specific equipments, some Artificial Intelligence (AI) technologies, such as case-based reasoning (CBR) and rule-based reasoning (RBR), were successfully introduced into RCM analysis process, and an intelligent RCM analysis system (IRCMAS) based on CBR and RBR was developed. The idea for such an intelligent system is based on the fact that the historical records of RCM analysis on similar items can be referenced and used for the current RCM analysis of a new item. Because many common or similar items may exist in the analyzed equipment, the repeated tasks of RCM analysis can be considerably simplified or avoided by means of the improvement of similar RCM cases in the IRCMAS. The application of the IRCMAS reduces the skill requirement of RCM analysts, shortens the development period of RCM program, and therefore enhances the cost-effectiveness of RCM process. We believe that the IRCMAS is a powerful tool for the development of RCM programs of physical assets, and has a potential future in the RCM market. The IRCMAS is substituting the traditional computer aided RCM system (CARCMS) within China's military industry, and is becoming the new generation RCM analysis tool for weapon systems under development. This paper describes the framework of the IRCMAS based on CBR and RBR, and discusses critical techniques in the system.