民用飞机维修规划的智能方法与技术研究
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摘要
航空工业是战略性高科技产业,中国自主研制的新型支线飞机已开始总装,大飞机项目也即将进入启动阶段,这对于提升航空工业在国民经济中的地位与影响具有举足轻重的作用。民用飞机是一项技术和资金密集、运营的安全性和经济性要求都很高、组成十分复杂的大型工程系统,设计阶段全机的维修任务规划对飞机全寿命的安全性、可靠性和经济性有非常重要的影响。民用飞机的维修任务规划包括全机各组成部分的故障影响及可靠性分析、维修工作确定及其间隔优化等内容。目前进行维修任务规划的主要方法有MSG方法、单部件维修建模方法(经典维修规划模型)和基于案例推理方法等三类。由于民用飞机维修规划工作的复杂性和繁琐性等原因,目前国际上进行民用飞机维修任务规划时,有效的方法实际上主要还是依靠原准机的经验数据和设计、维修人员的实际经验。因此,运用智能决策理论和计算机技术,研究民机维修任务规划的理论、智能决策方法和技术,对我国民用飞机研制具有重要理论意义和工程应用价值。
本论文对民机维修任务规划的智能决策方法和技术进行了系统的研究。研究核心是集成MSG方法、基于案例推理方法(CBR)和MAS技术解决维修任务规划的智能决策问题。
在维修规划理论方面,通过深入分析MSG、RCM等现代维修理论及其应用实践的基础,创造性地提出了MSG-3原理的数学表达方法,总结性地提出了维修规划的7个基本步骤,并给出了维修策略的总体决策流程。
在维修规划方法方面,综述了经典维修规划模型,将CBR方法应用到民用飞机的维修任务规划中,对维修规划CBR决策过程中的关键问题进行了系统地研究:首先研究了民用飞机的相似性问题,给出相似的定义和判断民用飞机相似的标准,然后,针对基于传统粗糙集进行属性选择的缺陷,提出采用基于相似粗糙集进行案例属性选择的方法。其次,给出了属性权重的计算方法,采用了属性空间的概念并给出了求解算法,在此基础上,提出了先采用分级式过滤检索算法粗选案例、再采用模糊近邻匹配方法精选案例的案例检索算法。再次,结合民用飞机维修规划的工程实际情况,采用了案例推理和规则推理相结合的集成推理模型,给出了维修规划的案例修改策略。最后对以上方法进行了实例验证。
在维修规划技术方面,针对进行维修规划时的分布式以及群体决策的特点,根据MAS的设计原理,研究了基于CBR和MAS的维修任务智能群决策系统模型,提出了基于规则推理与数学模型的案例修改模型。
在以上研究的基础上,设计并开发了民用飞机维修大纲群体决策系统。该系统已部分应用于正在研制的民用飞机ARJ21的维修大纲制订工作。
Aviation industry is a strategic high-tech industry. New Regional Jet Aircraft developed in China is assembling, and large aircraft project start soon, which is im-portant for advancing aviation industry’s station and effect. Civil aircraft is a tech-nique-intensive, capital-intensive, high safety and economy, quite complicated large project system, the aircraft maintenance task concept in design phase has very impor-tant effect for the safety, reliability and economic of life. Maintenance task concepts of the civil aircraft include failure effect and reliability analysis of every component, determination maintenance task and optimization interval et al. Presently the main methods of maintenance task concepts include MSG method, single part maintenance mathematic modeling (classical maintenance concept model) and case-based reason-ing et al. Due to the complexity and numerous of civil aircraft maintenance concept, presently the effective method is to relying to the experience data and design of origi-nal aircraft, the practice experience of maintenance personnel. Therefore, using intel-ligent decision making theory and computer technique to research the theory, intelli-gent decision making method and technical of maintenance task concept has impor-tant theory meaning and project foreground for civil aircraft developed in China.
Intelligent decision making method and technical of maintenance task concept of civil aircraft are researched. The core of research is to solve the intelligent deci-sion-making maintenance task concept integrating MSG method, CBR and multi-Agent System technic.
On the side of maintenance task concept theory, modern maintenance theory such as MSG and RCM, and its’appliance were analyzed deeply, the mathematic ex-pression method of MSG-3 theory was presented, the seven basic steps of mainte-nance task concept was put forward summarized, so did the total decision making process of maintenance policy.
On the side of maintenance task concept method, classical maintenance concept model were summarized, the CBR method was applied to maintenance task concept of civil aircraft. The key technical questions in the maintenance task concept deci-sion-making process were researched. At first, the similarity of civil aircraft was re-searched; the definition of similar and the standard of civil aircraft similar were put forward; then, aiming at the limitation of attribute selection basing on classic Rough Set, attribute selection basing on similarity Rough Set method was presented. Sec- ondly, the calculating method of attribute weight was presented, attribute special no-tion was adopted and its’arithmetic also presented. Based on these, the case retrieval arithmetic-first wide choose case using classification filtrate retrieval, then choiceness case using fuzzy near neighbour method was put forward. Thirdly, integration re-searching hybrid CBR and RBR was used based on the engineering facts of civil air-craft maintenance task concept, the case revision method of maintenance task concept was put forward. At last, all the methods were demonstrated.
On the side of maintenance task concept technical, maintenance task concept in-telligent Group decision-making system model based on CBR and MAS was re-searched, aimed at the distributing and group decision-making characteristic during maintenance task concept, and the theory of MAS. The case revision model based on RBR and mathematic model was presented.
Based on these researches, civil aircraft maintenance program group deci-sion-making Surport system was designed and developed, which is used in the main-tenance program developing of ARJ21 aircraft.
本论文对民机维修任务规划的智能决策方法和技术进行了系统的研究。研究核心是集成MSG方法、基于案例推理方法(CBR)和MAS技术解决维修任务规划的智能决策问题。
在维修规划理论方面,通过深入分析MSG、RCM等现代维修理论及其应用实践的基础,创造性地提出了MSG-3原理的数学表达方法,总结性地提出了维修规划的7个基本步骤,并给出了维修策略的总体决策流程。
在维修规划方法方面,综述了经典维修规划模型,将CBR方法应用到民用飞机的维修任务规划中,对维修规划CBR决策过程中的关键问题进行了系统地研究:首先研究了民用飞机的相似性问题,给出相似的定义和判断民用飞机相似的标准,然后,针对基于传统粗糙集进行属性选择的缺陷,提出采用基于相似粗糙集进行案例属性选择的方法。其次,给出了属性权重的计算方法,采用了属性空间的概念并给出了求解算法,在此基础上,提出了先采用分级式过滤检索算法粗选案例、再采用模糊近邻匹配方法精选案例的案例检索算法。再次,结合民用飞机维修规划的工程实际情况,采用了案例推理和规则推理相结合的集成推理模型,给出了维修规划的案例修改策略。最后对以上方法进行了实例验证。
在维修规划技术方面,针对进行维修规划时的分布式以及群体决策的特点,根据MAS的设计原理,研究了基于CBR和MAS的维修任务智能群决策系统模型,提出了基于规则推理与数学模型的案例修改模型。
在以上研究的基础上,设计并开发了民用飞机维修大纲群体决策系统。该系统已部分应用于正在研制的民用飞机ARJ21的维修大纲制订工作。
Aviation industry is a strategic high-tech industry. New Regional Jet Aircraft developed in China is assembling, and large aircraft project start soon, which is im-portant for advancing aviation industry’s station and effect. Civil aircraft is a tech-nique-intensive, capital-intensive, high safety and economy, quite complicated large project system, the aircraft maintenance task concept in design phase has very impor-tant effect for the safety, reliability and economic of life. Maintenance task concepts of the civil aircraft include failure effect and reliability analysis of every component, determination maintenance task and optimization interval et al. Presently the main methods of maintenance task concepts include MSG method, single part maintenance mathematic modeling (classical maintenance concept model) and case-based reason-ing et al. Due to the complexity and numerous of civil aircraft maintenance concept, presently the effective method is to relying to the experience data and design of origi-nal aircraft, the practice experience of maintenance personnel. Therefore, using intel-ligent decision making theory and computer technique to research the theory, intelli-gent decision making method and technical of maintenance task concept has impor-tant theory meaning and project foreground for civil aircraft developed in China.
Intelligent decision making method and technical of maintenance task concept of civil aircraft are researched. The core of research is to solve the intelligent deci-sion-making maintenance task concept integrating MSG method, CBR and multi-Agent System technic.
On the side of maintenance task concept theory, modern maintenance theory such as MSG and RCM, and its’appliance were analyzed deeply, the mathematic ex-pression method of MSG-3 theory was presented, the seven basic steps of mainte-nance task concept was put forward summarized, so did the total decision making process of maintenance policy.
On the side of maintenance task concept method, classical maintenance concept model were summarized, the CBR method was applied to maintenance task concept of civil aircraft. The key technical questions in the maintenance task concept deci-sion-making process were researched. At first, the similarity of civil aircraft was re-searched; the definition of similar and the standard of civil aircraft similar were put forward; then, aiming at the limitation of attribute selection basing on classic Rough Set, attribute selection basing on similarity Rough Set method was presented. Sec- ondly, the calculating method of attribute weight was presented, attribute special no-tion was adopted and its’arithmetic also presented. Based on these, the case retrieval arithmetic-first wide choose case using classification filtrate retrieval, then choiceness case using fuzzy near neighbour method was put forward. Thirdly, integration re-searching hybrid CBR and RBR was used based on the engineering facts of civil air-craft maintenance task concept, the case revision method of maintenance task concept was put forward. At last, all the methods were demonstrated.
On the side of maintenance task concept technical, maintenance task concept in-telligent Group decision-making system model based on CBR and MAS was re-searched, aimed at the distributing and group decision-making characteristic during maintenance task concept, and the theory of MAS. The case revision model based on RBR and mathematic model was presented.
Based on these researches, civil aircraft maintenance program group deci-sion-making Surport system was designed and developed, which is used in the main-tenance program developing of ARJ21 aircraft.
引文
[1] http://news.carnoc.com/list/84/84213.html
[2]包随义.放松管制扩大开放——中国民用航空运输业发展展望,中国民用航空, 2006, 71(11): 19-21
[3] http://bbs1.81tech.com/dispbbs.asp?boardID=103&ID=31385&star=6
[4] GJB3872-99.装备综合保障通用要求.中国人民解放军总装备部
[5] GJB1371-92,装备保障性分析.国防科学技术工业委员会
[6]徐宗昌,黄益嘉,杨宏伟.装备保障性工程与管理.北京:国防工业出版社, 2006
[7]章引平.论规划维修的主要内容和基本程序.航空标准化与质量, 2000, (4): 31-34
[8]章引平.规划维修的主要分析方法及其输入.航空标准化与质量, 2000, (5): 40-42
[9]甘茂治,康建设,高崎.军用装备维修工程学,北京,国防工业出版社, 1999: 212~218
[10] Waeyenbergh, G., Pintelon, L. A framework for maintenance concept development. Inter-national Journal of Production Economics 2002,77(3): 299–313
[11]吴建忠,何海龙,陈志兵等:维修思想发展综述,装备指挥技术学院学报, 2003, 14(3):20-23
[12] Nowlan F.S., Heap H.F., Reliability Centered Maintenance[M], Virginia: National Techni-cal Information Service, US Department of Commerce, 1978.(刘云、王立群等译)
[13] Moubray J., Reliability Centered Maintenance[M], New York: Oxford: Industrial Press Inc., 1997.(石磊谷宁昌译)
[14] American Bureau of Shipping, Guidance Notes for Reliability-centered Maintenance, U.S.A, 2004
[15] NASA, Reliability centered maintenance guide for facilities and collateral equipment. U.S.A, 2000
[16] Marvin Rausand: Reliability centered maintenance, Reliability Engineering and System Safety, 1998, 60(2).121-132
[17] Balbir.S. Dhillon, Engineering Management, CRC Press, 2002, 81-100
[18] Wireman T., Total Productive Maintenance: An American Approach, Industrial Press Inc, New York, 1991
[19] Nakajima S., TPM Development Program: Implementing Total Productive Maintenance, Productivity Press Inc., 1989
[20] Kathleen E. McKone, Roger G. Schroeder, Kristy O. Cua. Total productive maintenance: a contextual view, Journal of Operations Management, 1999,17(2): 123-144
[21] B.S. Blanchard, Logistics Engineering and Management, Prentice-Hall, Englewood Cliffs, NJ, 1992
[22] Niebel B W. Engineering Maintenance Management. Second Edition. New York: Marcel Dekker Inc.,1996
[23] British Standards Institution. BS3811:1993, British Standard Glossary of MaintenanceManagement Terms in Terotechnologh.1993
[24] Harker K. Power system commissioning and maintenance practice [R].London:The Institution of Electrical Engineers,1998
[25] Arjo Klijn.欧洲国家维修团体联盟的作用和重要性.中国设备工程,2001(11):53-55
[26] (日)高田敏则.面向21世纪的设备维修课题(一).设备管理与维修.2000, (5):46-47
[27] (日)高田敏则.面向21世纪的设备维修课题(二).设备管理与维修.2000, (6):45-46
[28] (日)高田敏则.面向21世纪的设备维修课题(三).设备管理与维修.2000, (7):42-44
[29] Eisinger S., Rakowsky U.K.. Modeling of uncertainties in reliability centered maintenance– a probabilistic approach, Reliability Engineering and System Safety, 2001, 71(2): 159-164
[30] Gabbar H.A., Yamashita H., Suzuki K. et al. Computer-aided RCM-based plant mainte-nance management system, Robotics and Computer-integrated Manufacturing, 2003, 19(5): 449-458
[31] Wang F.K., Lee W.. Learning curve analysis in total productive maintenance, Omega, 2001, 29 (6): 491-9
[32] Ireland F., Dale B.G.. A study of total productive maintenance implementation, Journal of Quality in Maintenance Engineering, 2001, 7(3): 183-91
[33] Das D.. Total predictive maintenance: a comprehensive tool for achieving excellence in operational systems, Industrial Engineering Journal, 2001, 30(10): 15-23.
[34] Nikolopoulos K., Metaxiotis K., Lekatis N. et al. Integrating industrial maintenance strat-egy into ERP”, Industrial Management and Data Systems, 2003, 103 (3): 184-191
[35] Wang H.. A survey of maintenance policies of deteriorating systems, European Journal of Operational Research, 2002, 139(3),:469-489
[36] Charles A., Floru I., Azzaro-Pantel C. el. Optimization of preventive maintenance strate-gies in a multipurpose batch plant: application to semi-conductor manufacturing, Com-puters & Chemical Engineering, 2003, 27(4): 449-467
[37] Chen C., Chen Y., Yuan J.. On a dynamic preventive maintenance policy for a system under inspection, Reliability Engineering and System Safety, 2003, 80(1): 41-47
[38] Bloch-Mercier S.. A preventive maintenance policy with sequential checking procedure for a Markov deteriorating system, European Journal of Operational Research, 2002, 142(3): 548-576
[39] Sheu S., Yeh R., Lin Y.. et. Al. A Bayesian approach to an adaptive preventive mainte-nance model, Reliability Engineering and System Safety, 2001, 71(1): 33-44
[40] Juang M., Anderson G.. A Bayesian method on adaptive preventive maintenance problem, European Journal of Operational Research, 2004, 155(2): 453-473
[41] Zhao Y.X.. On preventive maintenance policy of a critical reliability level for system subject to degradation, Reliability Engineering and System Safety, 2003, 79(3): 301-308
[42] Motta S., Branda?o D., Colosimo E.A.. Determination of preventive maintenance peri-odicities of standby devices, Reliability Engineering and System Safety, 2002, 76(2): 149-54
[43] McKone, K.E., Weiss E.E. Guidelines for implementing predictive maintenance, Produc-tion and Operations Management, 2002, 11(2): 109-124
[44] Chen D., Trivedi K.S.. Closed-form analytical results for condition-based maintenance, Reliability Engineering and System Safety, 2002, 76(1): 43-51
[45] Iravani S.M.R., Duenyas I. Integrated maintenance and production control of a deteriorat-ing production system”, IIE Transactions, 2002, 34(5): 423-435
[46] Jonsson P.. Company-wide integration of strategic maintenance: an empirical analysis”, International Journal of Production Economics, 1999, 60-61:155-164
[47] Dekker R., Scarf P.A. On the impact of optimization models in maintenance decision mak-ing: the state of the art, Reliability Engineering and System Safety, 1998, 60(2):111-119
[48] Apeland S., Scarf P.A. A fully subjective approach to modeling inspection maintenance, European Journal of Operational Research, 2003, 148(2):410-425
[49] Ho L.L., Silva A.F.,Unbiased estimators for mean time to failure and percentiles in a Weibull regression model, International Journal of Quality & Reliability Management, (2006), 23(3): 323-339
[50] Goel H.D., Grievink J., Weijnen M.P.C.. Integrated optimal reliable design, production, and maintenance planning for multipurpose process plants, Computers & Chemical Engi-neering, 2003, 27(11):1543-1555
[51] Al-Najjar B., Alsyouf I.. Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making, International Journal of Production Economics, 2003,. 84(1): 85-100
[52] Triantaphyllou E., Kovalerchuk B., Mann L.J. el, Determining the most important criteria in maintenance decision making, Journal of Quality in Maintenance Engineering, 1997, 3(1):16-28
[53] Mechefske C.K., Wang Z. Using fuzzy linguistics to select optimum maintenance and con-dition monitoring strategies, Mechanical Systems and Signal Processing, 2001, 15 (6): 1129-1140
[54] Mechefske C.K., Wang, Z. Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies, Mechanical Systems and Signal Processing, 2003, 17(2): 305-316
[55] Swanson L. An information processing model of maintenance management, International Journal of Production Economics, 2003, 83 (1): 45-64
[56] Chen T., Popova E.. Maintenance policies with two-dimensional warranty, Reliability En-gineering and System Safety, 2002, 77(1): 61-69
[57] Barata J., Soares C.G., Marseguerra M. et al.. Simulation modelling of repairable multi-component deteriorating systems for‘on condition’maintenance optimization, Reli-ability Engineering and System Safety, 2002, 76(3): 255-64
[58] Sarker R., Haque A.. Optimization of maintenance and spare provisioning policy using simulation, Applied Mathematical Modeling, 2000, 24(10): 751-760
[59] Balakrishnan N.T.. A simulation model for maintenance planning, Proceedings Annual Reliability and Maintainability Symposium IEEE, 1992:109-18
[60] Bruns P.. Optimal maintenance strategies for systems with partial repair options and with-out assuming bounded costs, European Journal of Operational Research, 2002, 139(1): 146-165
[61] Marquez A.C., Heguedas A.S.. Models for maintenance optimization: a study for repair-able systems and finite time periods, Reliability Engineering and System Safety, 2002, 75(3): 367-377
[62] Chiang J.H., Yuan J.. Optimal maintenance policy for a Markovian system under periodic inspection”, Reliability Engineering and System Safety, 2001, 71(2): 165-72
[63] Lam Y.. An optimal maintenance model for a combination of secondhand-new or out-dated-updated system, European Journal of Operational Research, 1999, 119(3): 739-752
[64] El Hayek M., van Voorthuysen E., Kelly D.W.. Optimizing life cycle cost of complex ma-chinery with rotable modules using simulation, Journal of Quality in Maintenance Engi-neering, 2005, 11(4): 333-347
[65] Andijani A., Duffuaa S.. Critical evaluation of simulation studies in maintenance, Produc-tion Planning and Control, 2002, 13(4): 336-341
[66] Bevilacqua M., Braglia M.. The analytic hierarchy process applied to maintenance strategy selection, Reliability Engineering and System Safety, 2000, 70(1):71-83
[67] Labib A.W.. World-class maintenance using a computerized maintenance management system, Journal of Quality in Maintenance Engineering, 1998, 4(1):66-75
[68] Rochdi Z., Driss B., Mohamed T.. Industrial systems maintenance modeling using Petri nets, Reliability Engineering and System Safety, 1999, 65(2):119-124
[69] Sherwin D.. A review of overall models for maintenance management, Journal of Quality in Maintenance Engineering, 2000, 6 (3): 138-64
[70] Bevilacqua M., Braglia M., Frosolini, M. el. Failure rate prediction with artificial neural networks, Journal of Quality in Maintenance Engineering, 2005, 11(3): 279-94
[71] Oien K.. Improved quality of input data for maintenance optimization using expert judgment, Reliability Engineering and System Safety, 1998, 60(2): 93-101
[72] Dieulle L., Berenguer C., Grall A. et al. Sequential condition-based maintenance schedul-ing for a deteriorating system, European Journal of Operational Research, 2003, 150(2): 451-461
[73] Grall A., Dieulle L., Berenguer C. et al. Continuous time predictive maintenance schedul-ing for a deteriorating system, IEEE Transactions on Reliability, 2002, 51(2): 141-150
[74] Gopalakrishnan M., Mohan S., He Z. A tabu search heuristic for preventive maintenance scheduling, Computers and Industrial Engineering, 2001, 40(1): 149-160
[75] Greenwood G., Gupta A. Workforce constrained preventive maintenance scheduling using evolution strategies, Decision Sciences, 2000, 31(4): 833-859
[76] Tsang A.H.C., Yeung W.K., Jardine,A.K.S. et al. Data management for CBM optimization, Journal of Quality in Maintenance Engineering, 2006, 12(1):37-51
[77] Artana K.B., Ishida K. Spreadsheet modeling of optimal maintenance schedule for com-ponents in wear-out phase, Reliability Engineering and System Safety, 2002, 77(1): 81-91
[78] Sloan T.W., Shanthikumar G.. Combined production and maintenance scheduling for a multiple product single machine production system, Production and Operations Manage-ment, 2000, 6(4): 379-399
[79] Duffuaa S.O., Al-Sultan K.S. A stochastic programming model for scheduling mainte-nance personnel, Applied Mathematical Modeling, 1999, 23(5: 385-397
[80] Tsang A.H.C., Jardine A.K.S., Kolodny H.. Measuring maintenance performance: a holis-tic approach, International Journal of Operations & Production Management, 1999, 19(7):691-715
[81] Arts R.H.P.M., Knapp G.M.J., Lawrence M. Some aspects of measuring maintenance per-formance in the process industry, Journal of Quality in Maintenance Engineering, 1998, 4(1): 6-11
[82] Groote P.D.. Maintenance performance analysis: a practical approach, Journal of Quality in Maintenance Engineering, 1995, 1(2): 4-24
[83] Swanson L. Linking maintenance strategies to performance, International Journal of Pro-duction Economics, 2001, 70(3): 237-244
[84] Pintelon L., Kumar P.S., Vereecke A.. Evaluating the effectiveness of maintenance strate-gies, Journal of Quality in Maintenance Engineering, 2006, 12(1):7-20
[85] Danny I Cho, Mahmut Parlar. A survey of maintenance models for multi-unit systems[J]. European Journal of Operational Research, 1991, 51: 1-23
[86] Baker R D, Christer A H.Review of delay-time OR modeling of engineering aspects of maintenance. European Journal of Operatlona1 Research, 1991, 73:407-422
[87] Dekker R. Applications of maintenance optimization models: A review and analysis. Reli-ability Engineering & Systems Safety, 1996, 51: 229-240
[88]周尚文,《设备维修管理的智能化》,钢铁技术.2006(2):35-38
[89]胡安定.设备维修与管理的新趋势.石油化工设备技术.1998, 19(1)
[90]涂忆柳,李晓东.维修工程管理研究与发展综述.工业工程与管理,2004,(4):7-12
[91] Fernandez O., Labib A.W., Walmisley R. et al. A decision support maintenance manage-ment system: development and implementation, International Journal of Quality & Reli-ability Management, 2003, 20(8):965-979
[92] Leger J.B., Movel G.. Integration of maintenance in the enterprise: towards an enterprise modeling based framework compliant with proactive maintenance strategy, Production Planning and Control, 2001, 12(2): 176-187
[93] Singer T. Are you using all the features of your CMMS? Following this 7-step plan can help uncover new benefits, Plant Engineering, 1999, 53(1): 32-34
[94] Labib A.W.. World-class maintenance using a computerized maintenance management system, Journal of Quality in Maintenance Engineering, 1998, 4(1): 66-75
[95] Swanson, L. Computerized maintenance management systems: a study of system design and use, Production and Inventory Management Journal, 1997, 38(2): 11-15
[96] Jones K., Collis S.. Computerized maintenance management systems, Property Manage-ment, 1996, 4(4): 33-37
[97] Pintelon L., Preez N.D., Puyvelde F.V.. Information technology: opportunities for mainte-nance management, Journal of Quality in Maintenance Engineering, 1999, 5(1): 9-24
[98] Satyanarayana B., Prasad J.K. Menu driven maintenance information system, Industrial Engineering Journal, 1996, 25(9): 8-11
[99] Nagarur N.N., Kaewplang J.. An object oriented decision support system for maintenance management, Journal of Quality in Maintenance Engineering, 1999, 5(3): 248-257
[100] Fitzgerald G., Philippides A., Probert S.. Information systems development: maintenance and enhancement: findings from a UK study”, International Journal of Information Man-agement, 1999, 19(4): 319-28
[101] Bardey D., Riane F., Artiba A.. To maintain or not to maintain? What should a risk-averse decision maker do?”, Journal of Quality in Maintenance Engineering, 2005, 11(2): 115-120
[102] Keller A Z. Giblin M T. Optimal maintenance and replacement policies. In Adv. Rel. Techn. Symp., Bradford, UK, Atomic Energy Authority, 1984
[103] Woodhouse J. Relating maintenance to production and company profits. In 6th Nat. Conf. on comp. for maint. Management, Conf. Communications. Monks Hill, Farnham, UK, 1986.
[104] Gestel P J, Kmoss P J. A maintenance optimization support system. In Proc. Scand. SRE Symp., Studsvik, Sweden, 1990
[105] Hontelez J A M, etc. Optimum condition-based maintenance policies for deteriorating sys-tems with partial information. Reliability Engineering & System Safety, 1996,51(3):267-274
[106] Jerkins A L. A decision support system for equipment maintenance and replacement. Op-erational Research'87, IFORS,1988:355-365
[107] Wang W. A prototype of software development in delay time modeling. Proceedings of '98 International Conference on Management Science &Engineering. Moscow, Russia, Harbin Institute of Technology Press, 1999:36-40
[108] Jardine A, Banjevic D, Makis V Optimal replace policy and structure of software for Con-diton-based maintenance. Journal of Quality in Maintenance Engineering. 1998,3(2):109-119
[109] Gerhard Vollmar, Zaijun Hu, Peter Bort. Decision support for root cause analysis in indus-trial environments. 8th IFAC Symposium on Analysis Design and Evaluation of Human Machine System. Kassel, Germany. 2001:487-493
[110] Stanislav Jovanovic, Malcolm Pearce. ECOTRACK: an overview of the system's func-tionality and implementation to date. Proceedings of the American Railway Engineering & Maintenance of Way Association Dallas, Texas September 10-13, 2000: 252-267
[111] Pieri G., Klein M.R., Milanese M.. A data and knowledge-based system for supporting the maintenance of chemical plant, International Journal of Production Economics, 2002, 79(2): 143-59
[112]王文彬,杨承,马天超.设备更新和大修理的理论与模型研究.管理科学研究集萃,国家自然科学基金委员会管理科学编.航天工业出版社,1993:271-291
[113]吴洪波.设备管理决策支持系统.中国管理科学,1999(2):51-56
[114]范世东.设备针对性维修策略及应用研究:[博士学位论文].大连:大连海事大学,1996
[115] Bonnisone P P, Johnson H E. Expert system for diesel electric locomotive repair: knowl-edge-based system report. General electric company: New York, 1983.
[116] Kobbacy K A H, Prodlove N L, Harper M A. Towards an intelligent maintenance optimi-zation system. J.Op1.Res.Soc., 1995(46):831-853.
[117] Zhang F, Jardine A. A smart maintenance decision system. In: Kobbacy KAH, Vadera S and Proudlove NC(eds). Proceedings of the First European Conference on Intelligent Man-agement Systems in Operations. Operational Research Society: Birminham, 1997:79-85.
[118] Yiliu Tu, Eddie H. H. Yeung. Intelligent decision support system for equipment diagnosis and maintenance management. The Third International Conference on Manufacturing Technology, Hong Kong, China, 1995:658-676
[119] Kobbacy KAH, Jeon J. The development of a hybrid intelligent maintenance optimization system(HIMOS). Journal of the Operational Research Society. 2001,52(7):762-778.Kobbacy KAH. On the evolution of an intelligent maintenance opti-mization system. Journal of the Operational Research Society. 2004,55(2):139-146
[120] Kobbacy KAH. On the evolution of an intelligent maintenance optimization system. Jour-nal of the Operational Research Society. 2004,55(2):139-146
[121] Daniel J. Fonseca. A knowledge-based system for preventive maintenance. Expert Sys-tem,2000,17(5):241-247
[122]方淑芬,吕文元.设备维修管理智能决策支持系统的研究.系统工程理论与实践,2001(12):53-59
[123]王长琼,孙国正.基于混合智能的金属结构诊断及维修决策系统.武汉理工大学学报(交通科学与工程版),2001,25(3):259-261
[124]林丽,马孝江.基于预知维修的设备管理决策支持系统的设计.机械,2004,31(4): 13-15.
[125]王善永,范文,钟敦美.基于状态监测的水电厂主设备检修计划决策系统.电力系统自动化,2001(8):45-47
[126]王险峰,李执力,杨华冰.装备维修智能决策支持系统的研究.设备维修与管理,2005(8):11-13
[127] [EB/OL]. http://www.reliasoft.cn/mpc/index.htm, 2005-06-15/2005-10-25
[128]罗延生.航空装备的RCM与计算机辅助分析的研究[博士学问论文].西安:西北工业大学管理学院,2000
[129] Hossam A Gabbar, Hiroyuki Yamashita, Kazuhiko Suzuki el at. Computer-aided RCM-based plant maintenance management system, Robotics and Computer-Integrated Manufacturing, 2003,19(5):449~458
[130]李希亮,王俊,贾希胜,等.基于案例的RCM分析系统研究.机械工程学院学报, 1999, 11(1):9~14
[131]王生楠,毛勇健.飞机结构EDR/ADR评定专家系统设计与实现.西北工业大学学报,2003,21(3):284~288
[132]耿端阳,左洪福,刘明等.民用飞机计划维修工作决策支持方法研究.航空学报, 2006, 27(5): 861-863
[133]蔡景.民用飞机系统维修规划方法研究[博士学问论文].南京:南京航空航天大学, 2007
[134]高红星.民用飞机维修大纲制订原理及方法研究[硕士学问论文].南京:南京航空航天大学, 2005
[135]陈小建.民机区域维修大纲制订方法研究与及辅助分析系统开发[硕士学问论文].南京:南京航空航天大学, 2006
[136]刘昕.飞机结构维修大纲制订方法研究及辅助软件开发[硕士学问论文].南京:南京航空航天大学, 2005
[137]王磊.基于可靠性的确定民机维修间隔的模型法研究[硕士学问论文].南京:南京航空航天大学, 2007
[138]尤嘉.基于CBR的民机维修大纲辅助决策系统研究[硕士学问论文].南京:南京航空航天大学, 2007
[139]诺兰,希普.以可靠性为中心的维修.北京:中国人们解放军空军第一研究所,1982
[140]莫布雷.以可靠性为中心的维修.北京:机械工业出版社,1995
[141] Waeyenbergh, G., Pintelon, L. Maintenance concept development: A case study[J]. Inter-national Journal of Production Economics.2004.89(3):395~405
[142]刘明,左洪福.航空维修策略的研究[J],飞机设计, 2007, 27(3):42-45
[143] Air Transport Association: MSG-3 Operator/Manufacturer Scheduled Maintenance De-velopment [S].U.S.A: Air Transport Association, Inc, 2005.15-47
[144]刘明,左洪福.航空维修思想的框架研究.航空维修与工程, 2007,51(5): 33-34
[145]周美立.相似性科学.北京:科学出版社,2004. 6-7
[146]周美立.相似系统论.北京:科学技术文献出版社,1994. 254-272
[147] Dash M, Liu H. Feature selection for classification. Intel Data A nal, 1997, 1(3): 131-156
[148] Kira K , Rendell LA. The feature selection problem: traditional methods and a new algo-rithm [A]. Proc Ninth National Conf on Artificial Intelligence. Anahein CA: AAA I Press, 1992. 129-134
[149] Langley P. Selection of relevant features in machine learning [A]. Proc AAA I Fall Sym on Relevance. New Orleans, LA: AAA I Press, 1994. 1-5
[150] Sebban M , Nock R.A hybrid flitter/wrapper approach of feature selection using informa-tion theory. Pattern Recognition, 2002,35:835-846
[151] Dominik S, Wojcjech Z. Attribute reduction in the Bayesian version of variable prevision rough set model. Electronic Notes in Theoretical Computer Science, 2003, 82(4): 1-11
[152] Jensen R., Shen Qiang.. Fuzzy rough attribute reduction with application to web categori-zation. Fuzzy Sets and Systems,2004, 141(3):469-485.
[153] Torkkola K. Feature extraction by non-parametric mutual information maximization. Jour-nal of Machine Learning Research, 2003,(3):1415-1438
[154] Globerson A , Tishby N. Sufficient dimensionality reduction. Journal of machine Learning Research, 2003, (3):1307-1331
[155] Kohavi R, Jo h n G. Wrappers for feature selection. Artificial Intelligence, 1997, (12): 273-324
[156] Forman G. An extensive empirical study o f feature selection metrics for text classification. Journal of Machine Learning Research, 2003,(3):1289-1306
[157] Bekkerman R, E l-Yaniv R, Tishby N, et al.Distributional world clusters vs worlds for text categorization. Journal of Machine Learning Research, 2003, (3):1183-1208
[158] Caruana R, de Sa V. Benefiting from the variables that variable selection discards. Journal of Machine Learning Research, 2003,(3):1245-1264
[159] Kohavi R, John G. W rappers for feature selection.Artificial Intelligence, 1997, (12): 273-324
[160] Pawlak, Z., Rough Sets. International J. of Computer and Sciences, 1982. 11(5): 341-356.
[161] Hu, X., N. Cercone. Mining knowledge rules from databases: a rough set approach. in Proc. of IEEE Int. Conf. on Data Engineering. 1996
[162] Tsumoto Sh, e.a. Extraction of Domain Knowledge from Databases Based on Rough Set Theory. in IEEE International Conference on Fuzzy Systems. 1996. New Jersey
[163] Nejman, D.A., Rough Set Based Method of Handwritten Numerals Classification. 1994, Warsaw University of Technology: Warsaw
[164]韩斌,吴铁军,杨晓辉.基于属性选择的因果网络多传感器融合系统.控制与决策, 2002. 17(6): 881-885
[165] Polkowski, L., A. Skowron. INtroducing rough metrological controllers: rough quality control. in Conference Proceedings (RSSC'94) the Third International workshop on Rough Sets and Soft Computing. 1994. Canada: San Jose State University
[166] Munakata, T. Rough control: Basic ideas and applications. in Second Annual Joint Con-ference on Information Science Proceedings. 1995. USA
[167] Lin, T.Y. Algebra and geometry of rough logic controllers. in The fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery. 1996. Tokyo: The Univer-sity of Tokyo
[168] Peters, J.F., Adaptive fuzzy rough approximate time controller design methodology: Con-cepts, Petri net model and application. in Proceedings of the IEEE International Confer-ence on Systems. 1998
[169] Mrozek, A. Methodology of Rough Controller Synthesis. in Proc. of IEEE International Conference on Fuzzy Systems. 1996. New Jersey
[170] Czogala, E.e.a., Idea of a Rough Fuzzy Controller and its Application to the Stabilization of a Pendulum-Car System. Fuzzy Sets and Systems, 1995. 72(1): 61-73
[171] Arima, M.e.a. Fuzzy Logic and Rough Sets Controller for HVAC Systems. in Proc. of IEEE WESCANEX Communications, Power, and Computing. 1995. New York
[172] Tsumoto, S. Automated Discovery of Medical Expert System Rules from Clinical Data-bases Based on Rough Sets. in Proc. of Second International Conf. on Knowledge dis-covery and Data Mining. 1996. USA
[173] Velasco, I., D. Teo, and T.Y. Lin. Design optimization of rough-fuzzy controllers using a genetic algorithm. in Joint conference of Information Sciences. 1997: Duke University
[174] Pawlak, Z., Rough set approach to knowledge-based decision support. European Journal of Operational Research, 1997: 1-10
[175] Pawlak, Z., R. Slowinski, Rough Set Approach to Multi-Attribute Decision Analysis. European Journal of Operational Research, 1994: 443-459
[176] Slowinski, R., Rough Set Approach to Decision Analysis. AI Expert, 1995: 19-25
[177] Aijun, A.e.a., Discovering Rules for Water Demand Prediction- an Enhanced Rough Set Approach. Engineering Applications of Artificial Intelligence, 1996. 9(6): 645
[178] Wojcik, Z.e.a. Application of Rough Sets for Edge Enhancing Image Filters. in Proc. of IEEE Int. conf. on Image Processing. 1994. USA
[179] Nguyen H. S., Sko wro n A.. Quantization of real values attributes, rough set and Boolean reasoning approaches. In:Proceedings of the 2nd Joint Annual Conference on Information Science, Wrightsville Beach, NC, 1995 , 34～37
[180] Nguyen S. H.., Nguyen H. S.. Some efficient algorithms for rough set methods. In: Pro-ceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spai n, 1996, 1451～1456
[181] Su smaga R.. A nalyzi ng discriminations of continuous s attributes given a monotonic discrimination function. Intelligent Data Analysis, 1997, 1( 4): 157～179
[182]侯利娟,王国胤,聂能,等.粗糙集理论中的离散化问题.计算机科学,2000,27(12): 89～94)
[183] Dai Jian Hua, Li Yuan Xiang. Study on discrimination based on rough set theory. In: Pro-ceedings of the first International Conference o n Machine Learning and Cybernetics, Bei-jing, 2002, 1371～1373
[184]王国胤. Rough集理论与知识获取.西安:西安交通大学出版社, 2001
[185] Chen Cai-Yun, Li Zhi-Guo, Qiao Sheng-Yo ng, Wen Shuo-Pi n . Study o n discrimination in rough set based on genetic algorithm. In: Proceeding s of the Second International Con-ference on Machine Learning and Cybernetics, Xi’an, 2003, 1430～1434
[186] Hua ng Jin Jie, Li Shi Yong . A GA based approach to rough data model. In: Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou. 2004, 1880～1884
[187]何亚群,胡寿松.粗糙集中连续属性离散化的一种新方法.南京航空航天大学学报,2003,35(3):213～215
[188]李兴生.一种基于云模型的决策表连续属性离散化方法.模式识别与人工智能, 2003, 16(3): 33～38.
[189] Roy A., Pal S. K.. Fuzzy discrimination of feature space for a rough set classier. Pattern Recognition Letters, 2003, 24(6):895～902.
[190] Wang Li-Hong, Zhang Shu-Cui, Fan Hui, Wu Geng-Feng. The information granulation in discretization. In:Proceedings of the Second International Conference on Machine Learn-ing and Cybernedcs, Xi’a n, 2003, 2620～2623
[191] Li Meng-Xin, Wu ChengDong, Han ZhongHua, Yue Yong. A hierarchical clustering method for attribute discrimination in rough set theory. In: Proceedings of the third Inter-national Conference on Machine Learning and Cybernetics, Shanghai, 2004,3650~3654
[192] Hen L., Tay E. H.. A discretization method for rough sets theory. Intelligent Data Analysis, 2001, 5(5): 431～438
[193] Tay E.H., Shen L.. A modified Chi2 algorithm for discrimination. IEEE Transactions on Knowledge and Data Engineering, 2002, 14(3): 666～670
[194] Su Chao Ton, Hsu Jyh-Hwa. An extended Chi2 algorithm for discrimination of real value attributes. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(3):437～441
[195] Air Transport Association of America, ATA Specification 2200, USA, 2003:153~155
[196] http://www.bh.com/companions/034074152X/appendices/data-a/default.htm
[197] Byung Kwon O. Meta web service: building web-based open decision support system based on web services [J]. Expert Systems with Applications, 2003,24(4):375~389
[198] Schank R, Abelson R R. Goals and Understanding[M],Erlbanum: Eksevier Science,1997,165-176
[199] Wittgenstein L, Philosophical Investigations, Blackwell, 1953:13-42
[200] Schank R. Dynamic Memory: A Theory of Reminding and Learning in Computers and People[M], London: Cambridge University Press, 1982
[201] Kolodner J. Maintaining organization in a dynamic long-term memory[J], Cognitive Sci-ence. 1983, 7(4): 243-280
[202]陈文伟,黄金才.数据仓库与数据挖掘。北京:人民邮电出版社,2004.1.204～217
[203] Liao T. W., Zhang Z. Similarity measures for retrieval in case-based reasoning systems. Applied Artificial Intelligence, 1998, 12( 1): 267~288
[204] Wilson D. R., Martinez T. R. Improved heterogeneous distance functions. Journal of Arti-ficial Intelligence Research, 1997, 6( 1): 1~34
[205] Dash M, Liu H. Feature selection for classification [J]. Intel Data A nal, 1997, 1(3): 131-156
[206] Kira K , Rende ll L A. T h e feature selection problem: traditional methods and a new al-gorithm. Proc Ninth National Conf on Artificial Intelligence [C]. Anahein CA: AAA I Press, 1992. 129-134
[207] Langley P. Selection of relevant features in machine learning. Proc AAA I Fall Sym on Relevance. New Orleans, LA: AAA I Press, 1994. 1-5
[208] Li Yongping, Chen Minye, Liu Ming. Estimation Method For Aircraft Similarity Based on Fuzziness Theory and Grey Incidence Analysis. Transactions of Nanjing University of Aeronautics & Astronautics, 2007, 24(3):194-198
[209] Serafim Opricovic. GWO-HSHJUNG TZENG.. Defuzzification within a multicriteria de-cision model [J], international journal of uncertainty, 2003, 11(5): 635-652
[210] Ming Liu, Hong Fu Zuo, Xian Cun Ni, Jing Cai. Research on a Case-Based Decision Support System for Aircraft Maintenance Review Board Report, ICIC2006, LNCS 4113, pp. 1030– 1039, Springer-Verlag Berlin Heidelberg, 2006.
[211] Liao Shu-Hsien. Expert system methodologies and applications-a decade review from 1995 to 2004, Expert Systems with Applications, 2005,28(1):93~103
[212] Shiu Simon C K, Pal Sankar K. Case-base reasoning: concepts, Features and Soft Com-puting, Applied Intelligence, 2004,21(3):233~238
[213] Wang Hei-Chia, Wang Huei–Sen. A hybrid expert system for equipment failure analys, Expert Systems with Applications, 2005,28(4):615~622
[214] Rissland E L, Skalak D. B.. CABARET: rule integration in a hybrid architecture. Interna-tional Journal of Man-Machine Studies, 1991, 34: 839-887
[215] Hammond K J. Explaining and repairing plans that fails. Artificial Intelligence, 1988, 45: 173-228
[216] Koton P A. Reasoning about evidence in casual explanations.In Proceedings of AAAI-88, Morgan Kaufmann, Los Altos, 1988, 256-261
[217] Rissland E L, Skalak D B. CABARET: rule integration in a hybrid architecture. Interna-tional Journal of Man-Machine Studies, 1991, 34: 839-887
[218] Golding A R, Renbloom P S. Improving rule-based systems through case-based reasoning. Proceedings ofAAAI’91, MIT Press, 1991, 22-27
[219] Aamodt A. A knowledge-intensive integrated approach to problem solving and sustained learning. Doctoral Dissertation, University of Trondheim, 1991
[220] Malek M, Rialle V. A case-based reasoning system applied to neuropathy diagnosis. In: Second European workshop[C]. Proceedings of EWCBR’94, Letures Notes in computer science. Springer Verlag, 1994, 329-336
[221] Bareiss E R, Wier C C. Protos: An exemplar-based learning apprentice. Proc. 4th Int. Workshop on machine learning, Irvine, California, 1987
[222] Babaka O, Whar S Y. Case-based reasoning and decision support systems. In: IEEE Inter-nal Conference on Intelligent Processing Systems., Beijing, 1997: 1532-1536
[223] Macchion D J, Vo D P. A hybrid knowledge-based systems for technical diagnosis learning and assistance. Proceedings of EWCBR’93, 1993, 301-312
[224] Lee Mal Rey. An Exception Handling of Rule-Based Reasoning Using Case-Based Rea-soning [J]. Journal of Intelligent and Robotic Systems, 2002,35(3):327~338
[225]刘明,左洪福,蔡景等.基于规则和案例的区域维修大纲专家系统设计与实现.见:可持续发展的中国交通论文集,北京:中国铁道出版社,2005.90-95
[226]刘明,左洪福,耿端阳等.基于RBR和CBR的维修大纲专家系统研究.北京航空航天大学学报, 2006, 32(5):521-525
[227] Mitra, R, Basak, J. Methods of case adaptation: a survey. International journal of intelli-gent systems, 2005, 20(6): 627-645
[228] Chang, CG, Cui, JJ, Wang, DW, et al. Research on case adaptation techniques in case-based reasoning. IN: Proceedings Of The 2004 International Conference On Machine Learning And Cybernetics, 2004,(1-7)
[229] R. Bergmann. Experience Retrieval. Lecture notes in computer science, 2002 (2432): 219-239
[230] Wilke W., Bergmann R. Techniques and knowledge used for adaptation during case-based problem solving. AngelPDelPobilJoséMiraMoonisAli. Tasks and Methods in Applied Arti-ficial Intelligence. Castellón, Spain, IEA/AIE, 1998:497-506
[231] Ming Liu, Hongfu Zuo. Research on Aircraft L/HIRF MRB Report Developing Method. Proceedings of the First International Conference on Maintenance Engineering, 2006: 210-215
[232]朱淼良,张新晖等.基于Agent的自主式智能机器人体系结构及集成系统.模式识别与人工智能,2001,13(1):36-40
[233]黄必清,刘文煌.基于智能Agent的群体决策支持系统及其在经营过程管理中的应用.系统工程理论与实践,2001,(4):74-78
[234] Franklin C, Graesser A.Is it an agent, or just a program:A taxonomy for autonomous agent. AAAI 1996,21-35;Prtrie C. What is an agent. AAAI 1996, 41-43
[235]曾平华,左召军,雄纯.多Agent装备故障诊断与维修系统研究.长沙航空职业技术学院学报,2006.6(3):21-24
[236] Ross, S.M., Applied Probability Modes With Optimization Applications, Holden-Day. San Francisco, 1970
[237]钱大琳,决策支持系统的人机关系研究.北方交通大学学报,2003.2(2): 22-25.
[238]林丽,马孝.基于预知维修的设备管理决策支持系统的设计.设备管理与维修, 2004(4): 8-9.
[2]包随义.放松管制扩大开放——中国民用航空运输业发展展望,中国民用航空, 2006, 71(11): 19-21
[3] http://bbs1.81tech.com/dispbbs.asp?boardID=103&ID=31385&star=6
[4] GJB3872-99.装备综合保障通用要求.中国人民解放军总装备部
[5] GJB1371-92,装备保障性分析.国防科学技术工业委员会
[6]徐宗昌,黄益嘉,杨宏伟.装备保障性工程与管理.北京:国防工业出版社, 2006
[7]章引平.论规划维修的主要内容和基本程序.航空标准化与质量, 2000, (4): 31-34
[8]章引平.规划维修的主要分析方法及其输入.航空标准化与质量, 2000, (5): 40-42
[9]甘茂治,康建设,高崎.军用装备维修工程学,北京,国防工业出版社, 1999: 212~218
[10] Waeyenbergh, G., Pintelon, L. A framework for maintenance concept development. Inter-national Journal of Production Economics 2002,77(3): 299–313
[11]吴建忠,何海龙,陈志兵等:维修思想发展综述,装备指挥技术学院学报, 2003, 14(3):20-23
[12] Nowlan F.S., Heap H.F., Reliability Centered Maintenance[M], Virginia: National Techni-cal Information Service, US Department of Commerce, 1978.(刘云、王立群等译)
[13] Moubray J., Reliability Centered Maintenance[M], New York: Oxford: Industrial Press Inc., 1997.(石磊谷宁昌译)
[14] American Bureau of Shipping, Guidance Notes for Reliability-centered Maintenance, U.S.A, 2004
[15] NASA, Reliability centered maintenance guide for facilities and collateral equipment. U.S.A, 2000
[16] Marvin Rausand: Reliability centered maintenance, Reliability Engineering and System Safety, 1998, 60(2).121-132
[17] Balbir.S. Dhillon, Engineering Management, CRC Press, 2002, 81-100
[18] Wireman T., Total Productive Maintenance: An American Approach, Industrial Press Inc, New York, 1991
[19] Nakajima S., TPM Development Program: Implementing Total Productive Maintenance, Productivity Press Inc., 1989
[20] Kathleen E. McKone, Roger G. Schroeder, Kristy O. Cua. Total productive maintenance: a contextual view, Journal of Operations Management, 1999,17(2): 123-144
[21] B.S. Blanchard, Logistics Engineering and Management, Prentice-Hall, Englewood Cliffs, NJ, 1992
[22] Niebel B W. Engineering Maintenance Management. Second Edition. New York: Marcel Dekker Inc.,1996
[23] British Standards Institution. BS3811:1993, British Standard Glossary of MaintenanceManagement Terms in Terotechnologh.1993
[24] Harker K. Power system commissioning and maintenance practice [R].London:The Institution of Electrical Engineers,1998
[25] Arjo Klijn.欧洲国家维修团体联盟的作用和重要性.中国设备工程,2001(11):53-55
[26] (日)高田敏则.面向21世纪的设备维修课题(一).设备管理与维修.2000, (5):46-47
[27] (日)高田敏则.面向21世纪的设备维修课题(二).设备管理与维修.2000, (6):45-46
[28] (日)高田敏则.面向21世纪的设备维修课题(三).设备管理与维修.2000, (7):42-44
[29] Eisinger S., Rakowsky U.K.. Modeling of uncertainties in reliability centered maintenance– a probabilistic approach, Reliability Engineering and System Safety, 2001, 71(2): 159-164
[30] Gabbar H.A., Yamashita H., Suzuki K. et al. Computer-aided RCM-based plant mainte-nance management system, Robotics and Computer-integrated Manufacturing, 2003, 19(5): 449-458
[31] Wang F.K., Lee W.. Learning curve analysis in total productive maintenance, Omega, 2001, 29 (6): 491-9
[32] Ireland F., Dale B.G.. A study of total productive maintenance implementation, Journal of Quality in Maintenance Engineering, 2001, 7(3): 183-91
[33] Das D.. Total predictive maintenance: a comprehensive tool for achieving excellence in operational systems, Industrial Engineering Journal, 2001, 30(10): 15-23.
[34] Nikolopoulos K., Metaxiotis K., Lekatis N. et al. Integrating industrial maintenance strat-egy into ERP”, Industrial Management and Data Systems, 2003, 103 (3): 184-191
[35] Wang H.. A survey of maintenance policies of deteriorating systems, European Journal of Operational Research, 2002, 139(3),:469-489
[36] Charles A., Floru I., Azzaro-Pantel C. el. Optimization of preventive maintenance strate-gies in a multipurpose batch plant: application to semi-conductor manufacturing, Com-puters & Chemical Engineering, 2003, 27(4): 449-467
[37] Chen C., Chen Y., Yuan J.. On a dynamic preventive maintenance policy for a system under inspection, Reliability Engineering and System Safety, 2003, 80(1): 41-47
[38] Bloch-Mercier S.. A preventive maintenance policy with sequential checking procedure for a Markov deteriorating system, European Journal of Operational Research, 2002, 142(3): 548-576
[39] Sheu S., Yeh R., Lin Y.. et. Al. A Bayesian approach to an adaptive preventive mainte-nance model, Reliability Engineering and System Safety, 2001, 71(1): 33-44
[40] Juang M., Anderson G.. A Bayesian method on adaptive preventive maintenance problem, European Journal of Operational Research, 2004, 155(2): 453-473
[41] Zhao Y.X.. On preventive maintenance policy of a critical reliability level for system subject to degradation, Reliability Engineering and System Safety, 2003, 79(3): 301-308
[42] Motta S., Branda?o D., Colosimo E.A.. Determination of preventive maintenance peri-odicities of standby devices, Reliability Engineering and System Safety, 2002, 76(2): 149-54
[43] McKone, K.E., Weiss E.E. Guidelines for implementing predictive maintenance, Produc-tion and Operations Management, 2002, 11(2): 109-124
[44] Chen D., Trivedi K.S.. Closed-form analytical results for condition-based maintenance, Reliability Engineering and System Safety, 2002, 76(1): 43-51
[45] Iravani S.M.R., Duenyas I. Integrated maintenance and production control of a deteriorat-ing production system”, IIE Transactions, 2002, 34(5): 423-435
[46] Jonsson P.. Company-wide integration of strategic maintenance: an empirical analysis”, International Journal of Production Economics, 1999, 60-61:155-164
[47] Dekker R., Scarf P.A. On the impact of optimization models in maintenance decision mak-ing: the state of the art, Reliability Engineering and System Safety, 1998, 60(2):111-119
[48] Apeland S., Scarf P.A. A fully subjective approach to modeling inspection maintenance, European Journal of Operational Research, 2003, 148(2):410-425
[49] Ho L.L., Silva A.F.,Unbiased estimators for mean time to failure and percentiles in a Weibull regression model, International Journal of Quality & Reliability Management, (2006), 23(3): 323-339
[50] Goel H.D., Grievink J., Weijnen M.P.C.. Integrated optimal reliable design, production, and maintenance planning for multipurpose process plants, Computers & Chemical Engi-neering, 2003, 27(11):1543-1555
[51] Al-Najjar B., Alsyouf I.. Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making, International Journal of Production Economics, 2003,. 84(1): 85-100
[52] Triantaphyllou E., Kovalerchuk B., Mann L.J. el, Determining the most important criteria in maintenance decision making, Journal of Quality in Maintenance Engineering, 1997, 3(1):16-28
[53] Mechefske C.K., Wang Z. Using fuzzy linguistics to select optimum maintenance and con-dition monitoring strategies, Mechanical Systems and Signal Processing, 2001, 15 (6): 1129-1140
[54] Mechefske C.K., Wang, Z. Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies, Mechanical Systems and Signal Processing, 2003, 17(2): 305-316
[55] Swanson L. An information processing model of maintenance management, International Journal of Production Economics, 2003, 83 (1): 45-64
[56] Chen T., Popova E.. Maintenance policies with two-dimensional warranty, Reliability En-gineering and System Safety, 2002, 77(1): 61-69
[57] Barata J., Soares C.G., Marseguerra M. et al.. Simulation modelling of repairable multi-component deteriorating systems for‘on condition’maintenance optimization, Reli-ability Engineering and System Safety, 2002, 76(3): 255-64
[58] Sarker R., Haque A.. Optimization of maintenance and spare provisioning policy using simulation, Applied Mathematical Modeling, 2000, 24(10): 751-760
[59] Balakrishnan N.T.. A simulation model for maintenance planning, Proceedings Annual Reliability and Maintainability Symposium IEEE, 1992:109-18
[60] Bruns P.. Optimal maintenance strategies for systems with partial repair options and with-out assuming bounded costs, European Journal of Operational Research, 2002, 139(1): 146-165
[61] Marquez A.C., Heguedas A.S.. Models for maintenance optimization: a study for repair-able systems and finite time periods, Reliability Engineering and System Safety, 2002, 75(3): 367-377
[62] Chiang J.H., Yuan J.. Optimal maintenance policy for a Markovian system under periodic inspection”, Reliability Engineering and System Safety, 2001, 71(2): 165-72
[63] Lam Y.. An optimal maintenance model for a combination of secondhand-new or out-dated-updated system, European Journal of Operational Research, 1999, 119(3): 739-752
[64] El Hayek M., van Voorthuysen E., Kelly D.W.. Optimizing life cycle cost of complex ma-chinery with rotable modules using simulation, Journal of Quality in Maintenance Engi-neering, 2005, 11(4): 333-347
[65] Andijani A., Duffuaa S.. Critical evaluation of simulation studies in maintenance, Produc-tion Planning and Control, 2002, 13(4): 336-341
[66] Bevilacqua M., Braglia M.. The analytic hierarchy process applied to maintenance strategy selection, Reliability Engineering and System Safety, 2000, 70(1):71-83
[67] Labib A.W.. World-class maintenance using a computerized maintenance management system, Journal of Quality in Maintenance Engineering, 1998, 4(1):66-75
[68] Rochdi Z., Driss B., Mohamed T.. Industrial systems maintenance modeling using Petri nets, Reliability Engineering and System Safety, 1999, 65(2):119-124
[69] Sherwin D.. A review of overall models for maintenance management, Journal of Quality in Maintenance Engineering, 2000, 6 (3): 138-64
[70] Bevilacqua M., Braglia M., Frosolini, M. el. Failure rate prediction with artificial neural networks, Journal of Quality in Maintenance Engineering, 2005, 11(3): 279-94
[71] Oien K.. Improved quality of input data for maintenance optimization using expert judgment, Reliability Engineering and System Safety, 1998, 60(2): 93-101
[72] Dieulle L., Berenguer C., Grall A. et al. Sequential condition-based maintenance schedul-ing for a deteriorating system, European Journal of Operational Research, 2003, 150(2): 451-461
[73] Grall A., Dieulle L., Berenguer C. et al. Continuous time predictive maintenance schedul-ing for a deteriorating system, IEEE Transactions on Reliability, 2002, 51(2): 141-150
[74] Gopalakrishnan M., Mohan S., He Z. A tabu search heuristic for preventive maintenance scheduling, Computers and Industrial Engineering, 2001, 40(1): 149-160
[75] Greenwood G., Gupta A. Workforce constrained preventive maintenance scheduling using evolution strategies, Decision Sciences, 2000, 31(4): 833-859
[76] Tsang A.H.C., Yeung W.K., Jardine,A.K.S. et al. Data management for CBM optimization, Journal of Quality in Maintenance Engineering, 2006, 12(1):37-51
[77] Artana K.B., Ishida K. Spreadsheet modeling of optimal maintenance schedule for com-ponents in wear-out phase, Reliability Engineering and System Safety, 2002, 77(1): 81-91
[78] Sloan T.W., Shanthikumar G.. Combined production and maintenance scheduling for a multiple product single machine production system, Production and Operations Manage-ment, 2000, 6(4): 379-399
[79] Duffuaa S.O., Al-Sultan K.S. A stochastic programming model for scheduling mainte-nance personnel, Applied Mathematical Modeling, 1999, 23(5: 385-397
[80] Tsang A.H.C., Jardine A.K.S., Kolodny H.. Measuring maintenance performance: a holis-tic approach, International Journal of Operations & Production Management, 1999, 19(7):691-715
[81] Arts R.H.P.M., Knapp G.M.J., Lawrence M. Some aspects of measuring maintenance per-formance in the process industry, Journal of Quality in Maintenance Engineering, 1998, 4(1): 6-11
[82] Groote P.D.. Maintenance performance analysis: a practical approach, Journal of Quality in Maintenance Engineering, 1995, 1(2): 4-24
[83] Swanson L. Linking maintenance strategies to performance, International Journal of Pro-duction Economics, 2001, 70(3): 237-244
[84] Pintelon L., Kumar P.S., Vereecke A.. Evaluating the effectiveness of maintenance strate-gies, Journal of Quality in Maintenance Engineering, 2006, 12(1):7-20
[85] Danny I Cho, Mahmut Parlar. A survey of maintenance models for multi-unit systems[J]. European Journal of Operational Research, 1991, 51: 1-23
[86] Baker R D, Christer A H.Review of delay-time OR modeling of engineering aspects of maintenance. European Journal of Operatlona1 Research, 1991, 73:407-422
[87] Dekker R. Applications of maintenance optimization models: A review and analysis. Reli-ability Engineering & Systems Safety, 1996, 51: 229-240
[88]周尚文,《设备维修管理的智能化》,钢铁技术.2006(2):35-38
[89]胡安定.设备维修与管理的新趋势.石油化工设备技术.1998, 19(1)
[90]涂忆柳,李晓东.维修工程管理研究与发展综述.工业工程与管理,2004,(4):7-12
[91] Fernandez O., Labib A.W., Walmisley R. et al. A decision support maintenance manage-ment system: development and implementation, International Journal of Quality & Reli-ability Management, 2003, 20(8):965-979
[92] Leger J.B., Movel G.. Integration of maintenance in the enterprise: towards an enterprise modeling based framework compliant with proactive maintenance strategy, Production Planning and Control, 2001, 12(2): 176-187
[93] Singer T. Are you using all the features of your CMMS? Following this 7-step plan can help uncover new benefits, Plant Engineering, 1999, 53(1): 32-34
[94] Labib A.W.. World-class maintenance using a computerized maintenance management system, Journal of Quality in Maintenance Engineering, 1998, 4(1): 66-75
[95] Swanson, L. Computerized maintenance management systems: a study of system design and use, Production and Inventory Management Journal, 1997, 38(2): 11-15
[96] Jones K., Collis S.. Computerized maintenance management systems, Property Manage-ment, 1996, 4(4): 33-37
[97] Pintelon L., Preez N.D., Puyvelde F.V.. Information technology: opportunities for mainte-nance management, Journal of Quality in Maintenance Engineering, 1999, 5(1): 9-24
[98] Satyanarayana B., Prasad J.K. Menu driven maintenance information system, Industrial Engineering Journal, 1996, 25(9): 8-11
[99] Nagarur N.N., Kaewplang J.. An object oriented decision support system for maintenance management, Journal of Quality in Maintenance Engineering, 1999, 5(3): 248-257
[100] Fitzgerald G., Philippides A., Probert S.. Information systems development: maintenance and enhancement: findings from a UK study”, International Journal of Information Man-agement, 1999, 19(4): 319-28
[101] Bardey D., Riane F., Artiba A.. To maintain or not to maintain? What should a risk-averse decision maker do?”, Journal of Quality in Maintenance Engineering, 2005, 11(2): 115-120
[102] Keller A Z. Giblin M T. Optimal maintenance and replacement policies. In Adv. Rel. Techn. Symp., Bradford, UK, Atomic Energy Authority, 1984
[103] Woodhouse J. Relating maintenance to production and company profits. In 6th Nat. Conf. on comp. for maint. Management, Conf. Communications. Monks Hill, Farnham, UK, 1986.
[104] Gestel P J, Kmoss P J. A maintenance optimization support system. In Proc. Scand. SRE Symp., Studsvik, Sweden, 1990
[105] Hontelez J A M, etc. Optimum condition-based maintenance policies for deteriorating sys-tems with partial information. Reliability Engineering & System Safety, 1996,51(3):267-274
[106] Jerkins A L. A decision support system for equipment maintenance and replacement. Op-erational Research'87, IFORS,1988:355-365
[107] Wang W. A prototype of software development in delay time modeling. Proceedings of '98 International Conference on Management Science &Engineering. Moscow, Russia, Harbin Institute of Technology Press, 1999:36-40
[108] Jardine A, Banjevic D, Makis V Optimal replace policy and structure of software for Con-diton-based maintenance. Journal of Quality in Maintenance Engineering. 1998,3(2):109-119
[109] Gerhard Vollmar, Zaijun Hu, Peter Bort. Decision support for root cause analysis in indus-trial environments. 8th IFAC Symposium on Analysis Design and Evaluation of Human Machine System. Kassel, Germany. 2001:487-493
[110] Stanislav Jovanovic, Malcolm Pearce. ECOTRACK: an overview of the system's func-tionality and implementation to date. Proceedings of the American Railway Engineering & Maintenance of Way Association Dallas, Texas September 10-13, 2000: 252-267
[111] Pieri G., Klein M.R., Milanese M.. A data and knowledge-based system for supporting the maintenance of chemical plant, International Journal of Production Economics, 2002, 79(2): 143-59
[112]王文彬,杨承,马天超.设备更新和大修理的理论与模型研究.管理科学研究集萃,国家自然科学基金委员会管理科学编.航天工业出版社,1993:271-291
[113]吴洪波.设备管理决策支持系统.中国管理科学,1999(2):51-56
[114]范世东.设备针对性维修策略及应用研究:[博士学位论文].大连:大连海事大学,1996
[115] Bonnisone P P, Johnson H E. Expert system for diesel electric locomotive repair: knowl-edge-based system report. General electric company: New York, 1983.
[116] Kobbacy K A H, Prodlove N L, Harper M A. Towards an intelligent maintenance optimi-zation system. J.Op1.Res.Soc., 1995(46):831-853.
[117] Zhang F, Jardine A. A smart maintenance decision system. In: Kobbacy KAH, Vadera S and Proudlove NC(eds). Proceedings of the First European Conference on Intelligent Man-agement Systems in Operations. Operational Research Society: Birminham, 1997:79-85.
[118] Yiliu Tu, Eddie H. H. Yeung. Intelligent decision support system for equipment diagnosis and maintenance management. The Third International Conference on Manufacturing Technology, Hong Kong, China, 1995:658-676
[119] Kobbacy KAH, Jeon J. The development of a hybrid intelligent maintenance optimization system(HIMOS). Journal of the Operational Research Society. 2001,52(7):762-778.Kobbacy KAH. On the evolution of an intelligent maintenance opti-mization system. Journal of the Operational Research Society. 2004,55(2):139-146
[120] Kobbacy KAH. On the evolution of an intelligent maintenance optimization system. Jour-nal of the Operational Research Society. 2004,55(2):139-146
[121] Daniel J. Fonseca. A knowledge-based system for preventive maintenance. Expert Sys-tem,2000,17(5):241-247
[122]方淑芬,吕文元.设备维修管理智能决策支持系统的研究.系统工程理论与实践,2001(12):53-59
[123]王长琼,孙国正.基于混合智能的金属结构诊断及维修决策系统.武汉理工大学学报(交通科学与工程版),2001,25(3):259-261
[124]林丽,马孝江.基于预知维修的设备管理决策支持系统的设计.机械,2004,31(4): 13-15.
[125]王善永,范文,钟敦美.基于状态监测的水电厂主设备检修计划决策系统.电力系统自动化,2001(8):45-47
[126]王险峰,李执力,杨华冰.装备维修智能决策支持系统的研究.设备维修与管理,2005(8):11-13
[127] [EB/OL]. http://www.reliasoft.cn/mpc/index.htm, 2005-06-15/2005-10-25
[128]罗延生.航空装备的RCM与计算机辅助分析的研究[博士学问论文].西安:西北工业大学管理学院,2000
[129] Hossam A Gabbar, Hiroyuki Yamashita, Kazuhiko Suzuki el at. Computer-aided RCM-based plant maintenance management system, Robotics and Computer-Integrated Manufacturing, 2003,19(5):449~458
[130]李希亮,王俊,贾希胜,等.基于案例的RCM分析系统研究.机械工程学院学报, 1999, 11(1):9~14
[131]王生楠,毛勇健.飞机结构EDR/ADR评定专家系统设计与实现.西北工业大学学报,2003,21(3):284~288
[132]耿端阳,左洪福,刘明等.民用飞机计划维修工作决策支持方法研究.航空学报, 2006, 27(5): 861-863
[133]蔡景.民用飞机系统维修规划方法研究[博士学问论文].南京:南京航空航天大学, 2007
[134]高红星.民用飞机维修大纲制订原理及方法研究[硕士学问论文].南京:南京航空航天大学, 2005
[135]陈小建.民机区域维修大纲制订方法研究与及辅助分析系统开发[硕士学问论文].南京:南京航空航天大学, 2006
[136]刘昕.飞机结构维修大纲制订方法研究及辅助软件开发[硕士学问论文].南京:南京航空航天大学, 2005
[137]王磊.基于可靠性的确定民机维修间隔的模型法研究[硕士学问论文].南京:南京航空航天大学, 2007
[138]尤嘉.基于CBR的民机维修大纲辅助决策系统研究[硕士学问论文].南京:南京航空航天大学, 2007
[139]诺兰,希普.以可靠性为中心的维修.北京:中国人们解放军空军第一研究所,1982
[140]莫布雷.以可靠性为中心的维修.北京:机械工业出版社,1995
[141] Waeyenbergh, G., Pintelon, L. Maintenance concept development: A case study[J]. Inter-national Journal of Production Economics.2004.89(3):395~405
[142]刘明,左洪福.航空维修策略的研究[J],飞机设计, 2007, 27(3):42-45
[143] Air Transport Association: MSG-3 Operator/Manufacturer Scheduled Maintenance De-velopment [S].U.S.A: Air Transport Association, Inc, 2005.15-47
[144]刘明,左洪福.航空维修思想的框架研究.航空维修与工程, 2007,51(5): 33-34
[145]周美立.相似性科学.北京:科学出版社,2004. 6-7
[146]周美立.相似系统论.北京:科学技术文献出版社,1994. 254-272
[147] Dash M, Liu H. Feature selection for classification. Intel Data A nal, 1997, 1(3): 131-156
[148] Kira K , Rendell LA. The feature selection problem: traditional methods and a new algo-rithm [A]. Proc Ninth National Conf on Artificial Intelligence. Anahein CA: AAA I Press, 1992. 129-134
[149] Langley P. Selection of relevant features in machine learning [A]. Proc AAA I Fall Sym on Relevance. New Orleans, LA: AAA I Press, 1994. 1-5
[150] Sebban M , Nock R.A hybrid flitter/wrapper approach of feature selection using informa-tion theory. Pattern Recognition, 2002,35:835-846
[151] Dominik S, Wojcjech Z. Attribute reduction in the Bayesian version of variable prevision rough set model. Electronic Notes in Theoretical Computer Science, 2003, 82(4): 1-11
[152] Jensen R., Shen Qiang.. Fuzzy rough attribute reduction with application to web categori-zation. Fuzzy Sets and Systems,2004, 141(3):469-485.
[153] Torkkola K. Feature extraction by non-parametric mutual information maximization. Jour-nal of Machine Learning Research, 2003,(3):1415-1438
[154] Globerson A , Tishby N. Sufficient dimensionality reduction. Journal of machine Learning Research, 2003, (3):1307-1331
[155] Kohavi R, Jo h n G. Wrappers for feature selection. Artificial Intelligence, 1997, (12): 273-324
[156] Forman G. An extensive empirical study o f feature selection metrics for text classification. Journal of Machine Learning Research, 2003,(3):1289-1306
[157] Bekkerman R, E l-Yaniv R, Tishby N, et al.Distributional world clusters vs worlds for text categorization. Journal of Machine Learning Research, 2003, (3):1183-1208
[158] Caruana R, de Sa V. Benefiting from the variables that variable selection discards. Journal of Machine Learning Research, 2003,(3):1245-1264
[159] Kohavi R, John G. W rappers for feature selection.Artificial Intelligence, 1997, (12): 273-324
[160] Pawlak, Z., Rough Sets. International J. of Computer and Sciences, 1982. 11(5): 341-356.
[161] Hu, X., N. Cercone. Mining knowledge rules from databases: a rough set approach. in Proc. of IEEE Int. Conf. on Data Engineering. 1996
[162] Tsumoto Sh, e.a. Extraction of Domain Knowledge from Databases Based on Rough Set Theory. in IEEE International Conference on Fuzzy Systems. 1996. New Jersey
[163] Nejman, D.A., Rough Set Based Method of Handwritten Numerals Classification. 1994, Warsaw University of Technology: Warsaw
[164]韩斌,吴铁军,杨晓辉.基于属性选择的因果网络多传感器融合系统.控制与决策, 2002. 17(6): 881-885
[165] Polkowski, L., A. Skowron. INtroducing rough metrological controllers: rough quality control. in Conference Proceedings (RSSC'94) the Third International workshop on Rough Sets and Soft Computing. 1994. Canada: San Jose State University
[166] Munakata, T. Rough control: Basic ideas and applications. in Second Annual Joint Con-ference on Information Science Proceedings. 1995. USA
[167] Lin, T.Y. Algebra and geometry of rough logic controllers. in The fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery. 1996. Tokyo: The Univer-sity of Tokyo
[168] Peters, J.F., Adaptive fuzzy rough approximate time controller design methodology: Con-cepts, Petri net model and application. in Proceedings of the IEEE International Confer-ence on Systems. 1998
[169] Mrozek, A. Methodology of Rough Controller Synthesis. in Proc. of IEEE International Conference on Fuzzy Systems. 1996. New Jersey
[170] Czogala, E.e.a., Idea of a Rough Fuzzy Controller and its Application to the Stabilization of a Pendulum-Car System. Fuzzy Sets and Systems, 1995. 72(1): 61-73
[171] Arima, M.e.a. Fuzzy Logic and Rough Sets Controller for HVAC Systems. in Proc. of IEEE WESCANEX Communications, Power, and Computing. 1995. New York
[172] Tsumoto, S. Automated Discovery of Medical Expert System Rules from Clinical Data-bases Based on Rough Sets. in Proc. of Second International Conf. on Knowledge dis-covery and Data Mining. 1996. USA
[173] Velasco, I., D. Teo, and T.Y. Lin. Design optimization of rough-fuzzy controllers using a genetic algorithm. in Joint conference of Information Sciences. 1997: Duke University
[174] Pawlak, Z., Rough set approach to knowledge-based decision support. European Journal of Operational Research, 1997: 1-10
[175] Pawlak, Z., R. Slowinski, Rough Set Approach to Multi-Attribute Decision Analysis. European Journal of Operational Research, 1994: 443-459
[176] Slowinski, R., Rough Set Approach to Decision Analysis. AI Expert, 1995: 19-25
[177] Aijun, A.e.a., Discovering Rules for Water Demand Prediction- an Enhanced Rough Set Approach. Engineering Applications of Artificial Intelligence, 1996. 9(6): 645
[178] Wojcik, Z.e.a. Application of Rough Sets for Edge Enhancing Image Filters. in Proc. of IEEE Int. conf. on Image Processing. 1994. USA
[179] Nguyen H. S., Sko wro n A.. Quantization of real values attributes, rough set and Boolean reasoning approaches. In:Proceedings of the 2nd Joint Annual Conference on Information Science, Wrightsville Beach, NC, 1995 , 34～37
[180] Nguyen S. H.., Nguyen H. S.. Some efficient algorithms for rough set methods. In: Pro-ceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spai n, 1996, 1451～1456
[181] Su smaga R.. A nalyzi ng discriminations of continuous s attributes given a monotonic discrimination function. Intelligent Data Analysis, 1997, 1( 4): 157～179
[182]侯利娟,王国胤,聂能,等.粗糙集理论中的离散化问题.计算机科学,2000,27(12): 89～94)
[183] Dai Jian Hua, Li Yuan Xiang. Study on discrimination based on rough set theory. In: Pro-ceedings of the first International Conference o n Machine Learning and Cybernetics, Bei-jing, 2002, 1371～1373
[184]王国胤. Rough集理论与知识获取.西安:西安交通大学出版社, 2001
[185] Chen Cai-Yun, Li Zhi-Guo, Qiao Sheng-Yo ng, Wen Shuo-Pi n . Study o n discrimination in rough set based on genetic algorithm. In: Proceeding s of the Second International Con-ference on Machine Learning and Cybernetics, Xi’an, 2003, 1430～1434
[186] Hua ng Jin Jie, Li Shi Yong . A GA based approach to rough data model. In: Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou. 2004, 1880～1884
[187]何亚群,胡寿松.粗糙集中连续属性离散化的一种新方法.南京航空航天大学学报,2003,35(3):213～215
[188]李兴生.一种基于云模型的决策表连续属性离散化方法.模式识别与人工智能, 2003, 16(3): 33～38.
[189] Roy A., Pal S. K.. Fuzzy discrimination of feature space for a rough set classier. Pattern Recognition Letters, 2003, 24(6):895～902.
[190] Wang Li-Hong, Zhang Shu-Cui, Fan Hui, Wu Geng-Feng. The information granulation in discretization. In:Proceedings of the Second International Conference on Machine Learn-ing and Cybernedcs, Xi’a n, 2003, 2620～2623
[191] Li Meng-Xin, Wu ChengDong, Han ZhongHua, Yue Yong. A hierarchical clustering method for attribute discrimination in rough set theory. In: Proceedings of the third Inter-national Conference on Machine Learning and Cybernetics, Shanghai, 2004,3650~3654
[192] Hen L., Tay E. H.. A discretization method for rough sets theory. Intelligent Data Analysis, 2001, 5(5): 431～438
[193] Tay E.H., Shen L.. A modified Chi2 algorithm for discrimination. IEEE Transactions on Knowledge and Data Engineering, 2002, 14(3): 666～670
[194] Su Chao Ton, Hsu Jyh-Hwa. An extended Chi2 algorithm for discrimination of real value attributes. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(3):437～441
[195] Air Transport Association of America, ATA Specification 2200, USA, 2003:153~155
[196] http://www.bh.com/companions/034074152X/appendices/data-a/default.htm
[197] Byung Kwon O. Meta web service: building web-based open decision support system based on web services [J]. Expert Systems with Applications, 2003,24(4):375~389
[198] Schank R, Abelson R R. Goals and Understanding[M],Erlbanum: Eksevier Science,1997,165-176
[199] Wittgenstein L, Philosophical Investigations, Blackwell, 1953:13-42
[200] Schank R. Dynamic Memory: A Theory of Reminding and Learning in Computers and People[M], London: Cambridge University Press, 1982
[201] Kolodner J. Maintaining organization in a dynamic long-term memory[J], Cognitive Sci-ence. 1983, 7(4): 243-280
[202]陈文伟,黄金才.数据仓库与数据挖掘。北京:人民邮电出版社,2004.1.204～217
[203] Liao T. W., Zhang Z. Similarity measures for retrieval in case-based reasoning systems. Applied Artificial Intelligence, 1998, 12( 1): 267~288
[204] Wilson D. R., Martinez T. R. Improved heterogeneous distance functions. Journal of Arti-ficial Intelligence Research, 1997, 6( 1): 1~34
[205] Dash M, Liu H. Feature selection for classification [J]. Intel Data A nal, 1997, 1(3): 131-156
[206] Kira K , Rende ll L A. T h e feature selection problem: traditional methods and a new al-gorithm. Proc Ninth National Conf on Artificial Intelligence [C]. Anahein CA: AAA I Press, 1992. 129-134
[207] Langley P. Selection of relevant features in machine learning. Proc AAA I Fall Sym on Relevance. New Orleans, LA: AAA I Press, 1994. 1-5
[208] Li Yongping, Chen Minye, Liu Ming. Estimation Method For Aircraft Similarity Based on Fuzziness Theory and Grey Incidence Analysis. Transactions of Nanjing University of Aeronautics & Astronautics, 2007, 24(3):194-198
[209] Serafim Opricovic. GWO-HSHJUNG TZENG.. Defuzzification within a multicriteria de-cision model [J], international journal of uncertainty, 2003, 11(5): 635-652
[210] Ming Liu, Hong Fu Zuo, Xian Cun Ni, Jing Cai. Research on a Case-Based Decision Support System for Aircraft Maintenance Review Board Report, ICIC2006, LNCS 4113, pp. 1030– 1039, Springer-Verlag Berlin Heidelberg, 2006.
[211] Liao Shu-Hsien. Expert system methodologies and applications-a decade review from 1995 to 2004, Expert Systems with Applications, 2005,28(1):93~103
[212] Shiu Simon C K, Pal Sankar K. Case-base reasoning: concepts, Features and Soft Com-puting, Applied Intelligence, 2004,21(3):233~238
[213] Wang Hei-Chia, Wang Huei–Sen. A hybrid expert system for equipment failure analys, Expert Systems with Applications, 2005,28(4):615~622
[214] Rissland E L, Skalak D. B.. CABARET: rule integration in a hybrid architecture. Interna-tional Journal of Man-Machine Studies, 1991, 34: 839-887
[215] Hammond K J. Explaining and repairing plans that fails. Artificial Intelligence, 1988, 45: 173-228
[216] Koton P A. Reasoning about evidence in casual explanations.In Proceedings of AAAI-88, Morgan Kaufmann, Los Altos, 1988, 256-261
[217] Rissland E L, Skalak D B. CABARET: rule integration in a hybrid architecture. Interna-tional Journal of Man-Machine Studies, 1991, 34: 839-887
[218] Golding A R, Renbloom P S. Improving rule-based systems through case-based reasoning. Proceedings ofAAAI’91, MIT Press, 1991, 22-27
[219] Aamodt A. A knowledge-intensive integrated approach to problem solving and sustained learning. Doctoral Dissertation, University of Trondheim, 1991
[220] Malek M, Rialle V. A case-based reasoning system applied to neuropathy diagnosis. In: Second European workshop[C]. Proceedings of EWCBR’94, Letures Notes in computer science. Springer Verlag, 1994, 329-336
[221] Bareiss E R, Wier C C. Protos: An exemplar-based learning apprentice. Proc. 4th Int. Workshop on machine learning, Irvine, California, 1987
[222] Babaka O, Whar S Y. Case-based reasoning and decision support systems. In: IEEE Inter-nal Conference on Intelligent Processing Systems., Beijing, 1997: 1532-1536
[223] Macchion D J, Vo D P. A hybrid knowledge-based systems for technical diagnosis learning and assistance. Proceedings of EWCBR’93, 1993, 301-312
[224] Lee Mal Rey. An Exception Handling of Rule-Based Reasoning Using Case-Based Rea-soning [J]. Journal of Intelligent and Robotic Systems, 2002,35(3):327~338
[225]刘明,左洪福,蔡景等.基于规则和案例的区域维修大纲专家系统设计与实现.见:可持续发展的中国交通论文集,北京:中国铁道出版社,2005.90-95
[226]刘明,左洪福,耿端阳等.基于RBR和CBR的维修大纲专家系统研究.北京航空航天大学学报, 2006, 32(5):521-525
[227] Mitra, R, Basak, J. Methods of case adaptation: a survey. International journal of intelli-gent systems, 2005, 20(6): 627-645
[228] Chang, CG, Cui, JJ, Wang, DW, et al. Research on case adaptation techniques in case-based reasoning. IN: Proceedings Of The 2004 International Conference On Machine Learning And Cybernetics, 2004,(1-7)
[229] R. Bergmann. Experience Retrieval. Lecture notes in computer science, 2002 (2432): 219-239
[230] Wilke W., Bergmann R. Techniques and knowledge used for adaptation during case-based problem solving. AngelPDelPobilJoséMiraMoonisAli. Tasks and Methods in Applied Arti-ficial Intelligence. Castellón, Spain, IEA/AIE, 1998:497-506
[231] Ming Liu, Hongfu Zuo. Research on Aircraft L/HIRF MRB Report Developing Method. Proceedings of the First International Conference on Maintenance Engineering, 2006: 210-215
[232]朱淼良,张新晖等.基于Agent的自主式智能机器人体系结构及集成系统.模式识别与人工智能,2001,13(1):36-40
[233]黄必清,刘文煌.基于智能Agent的群体决策支持系统及其在经营过程管理中的应用.系统工程理论与实践,2001,(4):74-78
[234] Franklin C, Graesser A.Is it an agent, or just a program:A taxonomy for autonomous agent. AAAI 1996,21-35;Prtrie C. What is an agent. AAAI 1996, 41-43
[235]曾平华,左召军,雄纯.多Agent装备故障诊断与维修系统研究.长沙航空职业技术学院学报,2006.6(3):21-24
[236] Ross, S.M., Applied Probability Modes With Optimization Applications, Holden-Day. San Francisco, 1970
[237]钱大琳,决策支持系统的人机关系研究.北方交通大学学报,2003.2(2): 22-25.
[238]林丽,马孝.基于预知维修的设备管理决策支持系统的设计.设备管理与维修, 2004(4): 8-9.