چارچوب برای تعمیر و نگهداری بر اساس قابلیت اطمینان هوشمند
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22390||2008||9 صفحه PDF||سفارش دهید||4680 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Reliability Engineering & System Safety, Volume 93, Issue 6, June 2008, Pages 806–814
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.
Reliability-centered maintenance (RCM), as a procedure to identify preventive maintenance (PM) requirements of complex systems, has been recognized and accepted in many industrial fields , , , ,  and , such as steel plant, aviation, railway network or ships maintenance. The countries applying RCM include the United States, Britain, Japan, etc. RCM was introduced into China in the late 1980s, and the first RCM standard GJB1378 was published and put into practice in 1992. Since then, RCM has been a popular methodology widely used in China's military to identify PM requirements of weapon systems. Numerous RCM programs of the in-service equipments have been developed. The major problem in the application of RCM is that the quality of RCM program is highly dependent on the experience and skills of the RCM analysts  and . In order to ensure the proper use of RCM, two efforts have been made in our application: (1) to strengthen the training of RCM group to ensure that the analysts have consistent understandings of RCM terms and principles; (2) to develop a computer aided RCM system (CARCMS) to ensure the consistency of the RCM procedures. Although the traditional CARCMS is a preferred tool for RCM analysts, it is not an intelligent system and cannot provide the similar cases for analysts. RCM practitioners were surveyed and responded noting need to access RCM cases of similar equipments or similar items for their reference in conducting RCM practice. A good RCM case reflects not only reliability data of the analyzed equipment, but also the principles and right understanding of RCM concepts. Based on the above considerations, case histories of approximately 70 RCM cases conducted in the past 10 years were compiled and analyzed. These RCM cases covered a wide range of ground weapon systems. RCM case includes all historical records in the RCM process, such as the list of functionally significant items (FSIA), the failure mode and effect analysis (FMEA) information and the RCM decision information. Apart from these, the basic reliability prediction information in current industry standard is also collected. At the same time, case-based reasoning (CBR) technology is explored and successfully introduced into RCM analysis process. A framework for intelligent RCM analysis (IRCMA) is studied, in which RCM cases and the basic reliability data of electrical and mechanical items are integrated. The (IRCMA) approach was implemented through an expert system.
نتیجه گیری انگلیسی
RCM analysis is a systematic engineering methodology to identify preventive maintenance (PM) requirement for equipments in many countries. RCM analysis process is a much repeated task, and dependent on RCM analysis experience. The IRCMA is feasible to improve efficiency and quality of RCM analysis. The study outcomes in this paper will further improve the accuracy and validity of RCM analysis, and have great significance on RCM popularization and application on military equipment and civilian facility. The next tasks of ours are to further perfect the IRCMAS. 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 of RCM analysis tool for weapon systems under development.