A framework for intelligent reliability centered maintenance analysis

• Published in: Reliability engineering & system safety Vol. 93; no. 6; pp. 806 - 814
• Main Authors: Cheng, Zhonghua, Jia, Xisheng, Gao, Ping, Wu, Su, Wang, Jianzhao
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.06.2008; Elsevier
• Subjects: Analysis system; Applied sciences; Case-based reasoning (CBR); Exact sciences and technology; Operational research and scientific management; Operational research. Management science; Reliability theory. Replacement problems; Reliability-centered maintenance (RCM); Case-based reasoning (CBR); Reliability-centered maintenance (RCM); Analysis system; Item analysis; Revision; Task scheduling; Maintenance; Case based reasoning; Reliability; System analysis; Artificial intelligence; Intelligent system
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2007.03.037

To improve the efficiency of reliability-centered maintenance (RCM) analysis, case-based reasoning (CBR), as a kind of artificial intelligence (AI) technology, was successfully introduced into RCM analysis process, and a framework for intelligent RCM analysis (IRCMA) was studied. The idea for IRCMA 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 revising the similar cases in conducting RCM analysis. Based on the previous theory studies, an intelligent RCM analysis system (IRCMAS) prototype was developed. This research has focused on the description of the definition, basic principles as well as a framework of IRCMA, and discussion of critical techniques in the IRCMA. Finally, IRCMAS prototype is presented based on a case study.