复杂系统的故障诊断及容错控制研究
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摘要
本文主要研究了基于多智能体系统方法对复杂系统进行故障诊断和容错控制的问题。首先,提出了采用多智能体系统对复杂系统进行分析、Gaia建模和故障诊断的方法;在具体的决策树故障诊断方法研究中,又提出了一种改进的二元决策图方法用于数字系统的多故障诊断;在多智能体系统的交互方式研究中,提出了基于本体的多智能体知识共享和协作方法用于建立飞机舵面故障诊断的知识库;最后结合神经网络和模糊建模的方法提出了基于多智能体系统的多故障容错方法,并用F-16飞机的舵面组合故障诊断与容错控制的仿真实例对以上各方法进行了验证。结果表明,本文提出的方法不但可以保证故障诊断系统的可靠性,还可以提高系统的容错性与实时性。全文主要内容如下:
在基于多智能体系统的复杂系统建模与故障诊断方法研究中,采用了Gaia方法对复杂系统进行建模,根据功能分析构建了故障诊断智能体的角色模型和交互模型,同时对该智能体的行为及各智能体之间进行交互协作的算法进行了详细设计,并针对某型飞机的舵面故障数据,根据各常用专家模型的置信度排序确定了可将决策树和神经网络做为具体的故障识别方法,最后分析了采用多智能体方法进行复杂系统故障诊断的功能和特点。
在具体的决策树故障诊断方法研究中,为了克服传统故障树分析法需要事先确定底事件顺序的局限性,本文首先通过在构件连接法的规则中加入了两项新规则,从而保证了由故障树转化的合成二元决策图(BDD)具有唯一的结构;接下来提出了割集的哈夫曼码与结构重要度的概念,这样在搜索故障源时,只要通过比较具有相同哈夫曼码长度的割集概率即可确定需要检测故障源的排序,而无需计算出所有割集的概率大小。该方法适用于具有独立底事件,且其故障模式可表示为故障树形式的数字系统的多故障诊断。由于不需要简化最终的合成BDD和确定故障的最小割集,因此这种方法比传统的故障树诊断方法具有更高的效率,并且更适合于在计算机上实现。
在多智能体系统的交互方式研究中,针对飞机系统故障诊断知识库获取与推理的问题,从系统工程的角度分析了飞机系统的复杂性,将飞机族的概念引入到飞机的本体建模中,并以舵面故障诊断过程为研究对象,用Protégé建立了飞机本体的领域知识模型,将单故障和组合故障的诊断知识列为本体中的SWRL规则,再通过JESS推理出的新知识得到诊断结果,实现了用本体来选择修复方案的过程。该方法能够实现复杂系统的本体建模以及故障诊断方案的准确选择,并可通过增加新的诊断知识来完善故障诊断知识库。
在多智能体系统容错控制的研究中,提出了容错多智能体系统(FATMAS)的相关概念和在FATMAS中实现多故障容错的各类函数定义。通过调用不同函数可对系统中的多故障进行检测,并可通过复制非关键智能体的任务使系统从多故障中恢复运行,列出了相关流程,并在JACK平台上实现了该方法在F-16飞机舵面组合故障诊断与自修复中的应用。该方法可用于复杂系统的多故障诊断与容错,为智能体的交互提供了清晰的消息传递机制,并有效减少了系统中复制智能体的数目,比较接近于实际系统中的故障处理过程。
最后,通过对现有多智能体开发平台的分析和选择以及故障诊断智能体模型的建立,实现了基于JADE平台和Simulink平台对飞机舵面结构故障的在线监控与诊断系统,并采用MACSim实现了两个平台之间的信息互通,整个系统在系统维护、实时故障监控和诊断能力上有了一定的提高,并可实现组合故障的协同诊断,从而达到要求的性能指标。
This paper studies the method based on multi-agent system for fault diagnosis and tolerance of complex systems. First, the approach based on multi-agent system for analysis, Gaia modeling and fault diagnosis of complex system is put forward. And in the research of fault diagnosis, an advanced algorithm of binary decision diagram (BDD) is proposed to speed up the search of fault source of fault trees. Then the method of establishment and reasoning of fault diagnosis knowledge using ontology language is also proposed to improve the understanding among agents during their interaction, and this method can be used to build the knowledge base of fault diagnosis of aircraft control surface. Finally, the method of multiple-fault diagnosis based on multi-agent system combining neural network and fuzzy control is studied. And all the methods are verified by simulation using examples of combination fault diagnosis and fault-tolerant control of F-16 aircraft control surface. The main content of this paper is as follows:
In the research of modeling and fault diagnosis of complex systems based on multi-agent system, the Gaia method is used to modeling complex system. Then the roll models and interaction models of fault diagnosis agent are analyzed and built according to their functionalities. At the same time, the behavior and cooperation algorithm are designed in detail for this agent. And the function and characteristics of fault diagnosis for complex systems based on multi-agent system are also analyzed.
In the research of specified method of fault diagnosis, in order to overcome the limitation of the requirement of a predetermined sequence of basic events, two new rules are added to the connection rules of component connection approach for fault tree conversion to Binary Decision Diagram (BDD) to ensure the unique structure of the final integrated BDD. Then through comparing the probabilities of cut sets with the same length of Huffman code, the ordering of checking the fault source can be determined. This method can be applied to digital systems of which the multiple-fault mechanism can be expressed by fault trees with independent basic events. Because this method has no need to simplify the integrated BDD and determine the minimal cut sets, it’s more suitable for computer execution and has higher efficiency than traditional methods of fault tree diagnosis.
In the research of interactions among multi agents, for the problems of acquisition and reasoning of fault diagnosis knowledge, the complexity of aircraft system was analyzed from the engineering point of view and the concept of aircraft family was introduced to the ontology modeling of aircraft. At the same time, the fault diagnosis of aircraft control surface was also studied. First, the domain knowledge model of aircraft ontology was built by Protégé. Then the knowledge of single fault and combined faults diagnosis were listed as SWRL rules. Finally, the diagnosis result could be concluded through JESS reasoning to select the right plan of self-repairing. This method can be used to realize the modeling of complex systems and accurate selection of fault diagnosis plan. And the knowledge base of fault diagnosis can also be improved by adding new rules.
In the research of fault tolerance of multi-agent system, the basic concepts of multiple-agent systems (MAS) are expanded to build the related concepts of fault tolerant multi-agent system (FATMAS). And based on all these concepts, the definitions of various functions are also achieved to realize multiple fault tolerance in FATMAS. The multiple faults can be detected by calling different functions of the system, and the system can recover from multiple faults to run by copying the tasks of non-critical agents. The relevant flows are listed and an example is given to illustrate the application of this method in the aircraft multiple fault diagnosis and self-repairing in JACK platform. This method can be used in complex system for multiple fault diagnosis and tolerance. The clear inter-agent message passing mechanism is also provided and the number of replicated agents is effectively reduced. So this method is relatively close to the process of dealing with failures in actual system.
In the end, through the analysis and selection of existing multi-agent development platforms, and the modeling of fault diagnosis agent, the online fault diagnosis and monitoring systems of aircraft control surface are realized based on JADE and Simulink. During this process, MACSim is used to realize the information exchange between the two platforms. The system maintenance, real-time fault diagnosis and monitoring capacity of the whole system has been improved to some degree, and the coordinated fault diagnosis of combined faults can be realized to achieve the required performance.
在基于多智能体系统的复杂系统建模与故障诊断方法研究中,采用了Gaia方法对复杂系统进行建模,根据功能分析构建了故障诊断智能体的角色模型和交互模型,同时对该智能体的行为及各智能体之间进行交互协作的算法进行了详细设计,并针对某型飞机的舵面故障数据,根据各常用专家模型的置信度排序确定了可将决策树和神经网络做为具体的故障识别方法,最后分析了采用多智能体方法进行复杂系统故障诊断的功能和特点。
在具体的决策树故障诊断方法研究中,为了克服传统故障树分析法需要事先确定底事件顺序的局限性,本文首先通过在构件连接法的规则中加入了两项新规则,从而保证了由故障树转化的合成二元决策图(BDD)具有唯一的结构;接下来提出了割集的哈夫曼码与结构重要度的概念,这样在搜索故障源时,只要通过比较具有相同哈夫曼码长度的割集概率即可确定需要检测故障源的排序,而无需计算出所有割集的概率大小。该方法适用于具有独立底事件,且其故障模式可表示为故障树形式的数字系统的多故障诊断。由于不需要简化最终的合成BDD和确定故障的最小割集,因此这种方法比传统的故障树诊断方法具有更高的效率,并且更适合于在计算机上实现。
在多智能体系统的交互方式研究中,针对飞机系统故障诊断知识库获取与推理的问题,从系统工程的角度分析了飞机系统的复杂性,将飞机族的概念引入到飞机的本体建模中,并以舵面故障诊断过程为研究对象,用Protégé建立了飞机本体的领域知识模型,将单故障和组合故障的诊断知识列为本体中的SWRL规则,再通过JESS推理出的新知识得到诊断结果,实现了用本体来选择修复方案的过程。该方法能够实现复杂系统的本体建模以及故障诊断方案的准确选择,并可通过增加新的诊断知识来完善故障诊断知识库。
在多智能体系统容错控制的研究中,提出了容错多智能体系统(FATMAS)的相关概念和在FATMAS中实现多故障容错的各类函数定义。通过调用不同函数可对系统中的多故障进行检测,并可通过复制非关键智能体的任务使系统从多故障中恢复运行,列出了相关流程,并在JACK平台上实现了该方法在F-16飞机舵面组合故障诊断与自修复中的应用。该方法可用于复杂系统的多故障诊断与容错,为智能体的交互提供了清晰的消息传递机制,并有效减少了系统中复制智能体的数目,比较接近于实际系统中的故障处理过程。
最后,通过对现有多智能体开发平台的分析和选择以及故障诊断智能体模型的建立,实现了基于JADE平台和Simulink平台对飞机舵面结构故障的在线监控与诊断系统,并采用MACSim实现了两个平台之间的信息互通,整个系统在系统维护、实时故障监控和诊断能力上有了一定的提高,并可实现组合故障的协同诊断,从而达到要求的性能指标。
This paper studies the method based on multi-agent system for fault diagnosis and tolerance of complex systems. First, the approach based on multi-agent system for analysis, Gaia modeling and fault diagnosis of complex system is put forward. And in the research of fault diagnosis, an advanced algorithm of binary decision diagram (BDD) is proposed to speed up the search of fault source of fault trees. Then the method of establishment and reasoning of fault diagnosis knowledge using ontology language is also proposed to improve the understanding among agents during their interaction, and this method can be used to build the knowledge base of fault diagnosis of aircraft control surface. Finally, the method of multiple-fault diagnosis based on multi-agent system combining neural network and fuzzy control is studied. And all the methods are verified by simulation using examples of combination fault diagnosis and fault-tolerant control of F-16 aircraft control surface. The main content of this paper is as follows:
In the research of modeling and fault diagnosis of complex systems based on multi-agent system, the Gaia method is used to modeling complex system. Then the roll models and interaction models of fault diagnosis agent are analyzed and built according to their functionalities. At the same time, the behavior and cooperation algorithm are designed in detail for this agent. And the function and characteristics of fault diagnosis for complex systems based on multi-agent system are also analyzed.
In the research of specified method of fault diagnosis, in order to overcome the limitation of the requirement of a predetermined sequence of basic events, two new rules are added to the connection rules of component connection approach for fault tree conversion to Binary Decision Diagram (BDD) to ensure the unique structure of the final integrated BDD. Then through comparing the probabilities of cut sets with the same length of Huffman code, the ordering of checking the fault source can be determined. This method can be applied to digital systems of which the multiple-fault mechanism can be expressed by fault trees with independent basic events. Because this method has no need to simplify the integrated BDD and determine the minimal cut sets, it’s more suitable for computer execution and has higher efficiency than traditional methods of fault tree diagnosis.
In the research of interactions among multi agents, for the problems of acquisition and reasoning of fault diagnosis knowledge, the complexity of aircraft system was analyzed from the engineering point of view and the concept of aircraft family was introduced to the ontology modeling of aircraft. At the same time, the fault diagnosis of aircraft control surface was also studied. First, the domain knowledge model of aircraft ontology was built by Protégé. Then the knowledge of single fault and combined faults diagnosis were listed as SWRL rules. Finally, the diagnosis result could be concluded through JESS reasoning to select the right plan of self-repairing. This method can be used to realize the modeling of complex systems and accurate selection of fault diagnosis plan. And the knowledge base of fault diagnosis can also be improved by adding new rules.
In the research of fault tolerance of multi-agent system, the basic concepts of multiple-agent systems (MAS) are expanded to build the related concepts of fault tolerant multi-agent system (FATMAS). And based on all these concepts, the definitions of various functions are also achieved to realize multiple fault tolerance in FATMAS. The multiple faults can be detected by calling different functions of the system, and the system can recover from multiple faults to run by copying the tasks of non-critical agents. The relevant flows are listed and an example is given to illustrate the application of this method in the aircraft multiple fault diagnosis and self-repairing in JACK platform. This method can be used in complex system for multiple fault diagnosis and tolerance. The clear inter-agent message passing mechanism is also provided and the number of replicated agents is effectively reduced. So this method is relatively close to the process of dealing with failures in actual system.
In the end, through the analysis and selection of existing multi-agent development platforms, and the modeling of fault diagnosis agent, the online fault diagnosis and monitoring systems of aircraft control surface are realized based on JADE and Simulink. During this process, MACSim is used to realize the information exchange between the two platforms. The system maintenance, real-time fault diagnosis and monitoring capacity of the whole system has been improved to some degree, and the coordinated fault diagnosis of combined faults can be realized to achieve the required performance.
引文
[1]曾儒伟,许诚,曾亮.故障诊断方法发展动向.航空计算技术,2003,33(3):19-22.
[2] Frank P. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. Automatica, 1990,26(3):459-474.
[3] Ding S. Model-based fault diagnosis techniques: design schemes, algorithms, and tools, Springer Verlag, 2008.
[4] Isermann R. Model-based fault-detection and diagnosis-status and applications. Annual Reviews in control, 2005,29(1):71-85.
[5]胡寿松,周川,王源.基于小波神经网络的组合故障模式识别.自动化学报,2002,28(4):540-543.
[6]余红英,潘宏侠.基于分形的机械故障诊断方法研究.华北工学院学报,2005,26(2): 115-118.
[7]李霁红,康锐,贾颖.复杂系统的模糊故障诊断方法研究.系统工程与电子技术,2005, 27(7):1322-1324.
[8]杨帆,萧德云. SDG建模及其应用的进展.控制理论与应用,2005,22(5):767-774.
[9]李冬辉,刘浩.基于概率神经网络的故障诊断方法及应用.系统工程与电子技术,2004, 26(7):997-999.
[10]李青,史雅琴,周扬.基于案例推理方法在飞机故障诊断中的应用.北京航空航天大学学报,2007,33(5):622-626.
[11]徐波,于劲松,李行善.复杂系统的智能故障诊断.信息与控制,2004,33(1):56-60.
[12]张晓阳.面向复杂系统生命周期的故障诊断技术研究.南京:南京理工大学博士论文,2005.
[13]Russell S, Norvig P. Artificial intelligence: A modern approach. New Jersey: Pearson Education, 1995.
[14]Maes P. Artificial life meets entertainment: lifelike autonomous agents. Communications of the ACM,1995,38(11):108-114.
[15]Smith D, Cypher A and Spohrer J. KidSim: programming agents without a programming language. Communications of the ACM,1994,37(7):54-67.
[16]Hayes-Roth B. An architecture for adaptive intelligent systems. Artificial Intelligence,1995,72(1-2):329-365.
[17]Wooldridge M. and Jennings N. Agent theories, architectures, and languages: a survey. Intelligent agents,1995(890):1-39.
[18]Sycara K. Multiagent systems. AI magazine,1998,19(2):79-92.
[19]Wooldridge M. An introduction to multiagent systems. UK:Wiley,2009.
[20]Wooldridge M and Jennings N. Intelligent agents: Theory and practice. The Knowledge Engineering Review,2009,10(2):115-152.
[21]Gechter F, Chevrier V and Charpillet F. A reactive agent-based problem-solving model: Application to localization and tracking. ACM Transactions on Autonomous and Adaptive Systems (TAAS),2006,1(2):222.
[22]Bonasso R, Firby R, Gat E, Kortenkamp D, Miller D and Slack M. Experiences with an architecture for intelligent, reactive agents. Journal of Experimental &Theoretical Artificial Intelligence,1997,9(2):237-256.
[23]Guessoum Z. A hybrid agent model: a reactive and cognitive behavior. IEEE Computer Society:1997.
[24]史忠植.智能主体及其应用.北京:科学出版社, 2000.
[25]廖守亿.复杂系统基于Agent的建模与仿真方法研究及应用.长沙:国防科学技术大学博士论文,2005.
[26]Jennings N, Sycara K and Wooldridge M. A roadmap of agent research and development. Autonomous agents and multi-agent systems,1998,1(1):7-38.
[27]Wooldridge M. An introduction to multiagent systems. UK:Wiley,2009.
[28]Nwana H. Software agents: An overview. The Knowledge Engineering Review,2009, 11(3): 205-244.
[29]Caglayan A, Harrison C and Harrison C. Agent Source Book–a complete guide to Desktop, Internet and Intranet Agents.UK:Wiley, 1997.
[30]Tumer K and Agogino A. Distributed agent-based air traffic flow management. ACM, 2007.
[31]张欧亚,佟明安,马瑞萍,温孚禄.人机一体化的巡航导弹监控Agent设计.系统工程理论与实践,2007,27(9):159-164.
[32]Baxter J, Horn G and Leivers D. Fly-by-agent: Controlling a pool of UAVs via a multi-agent system. Knowledge-Based Systems,2008,21(3):232-237.
[33]宗令蓓,谢凡,秦世引.基于MAS的无人机编队飞行智能优化控制.航空学报,2008, 29(5):1326-1333.
[34]Ota J. Multi-agent robot systems as distributed autonomous systems. Advanced engineering informatics,2006,20(1):59-70.
[35]Ming Z, Jianwen R, Gengyin L, Xiang-hai X, Zhi-xu C, Jing-huai L and Gui-zhong Y. A Multi-Agent based dispatching operation instructing system in electric power systems.Proceedings of Chinese Society of Electrical Engineering,2004, 24(4):58-62.
[36]Viamonte M, Ramos C, Rodrigues F and Cardoso J. ISEM: a multiagent Simulator for testing agent market strategies. IEEE Transactions on Systems, Man and Cybernetics-Part C: applications and reviews,2006,36(1):107.
[37]Caridi M and Cavalieri S. Multi-agent systems in production planning and control: an overview. Production Planning & Control,2004,15(2):106-118.
[38]Klusch M. Information agent technology for the internet: A survey. Data & Knowledge Engineering,2001,36(3):337-372.
[39]Van Dyke Parunak H, Savit R and Riolo R. Agent-based modeling vs. equation-based modeling: A case study and users' guide. Springer,1998: 10-25.
[40]Bonabeau E. Agent-based modeling: Methods and techniques for simulating human systems.National Acad Sciences,2002:7280.
[41]Huhns M and Stephens L. Multiagent Systems and Societies of Agents,2008.
[42]毛新军.面向主体的软件开发.北京:清华大学出版社,2005.
[43]DeLoach S. Analysis and Design using MaSE and agentTool.Citeseer,2001.
[44]Gervais M. ODAC: An agent-oriented methodology based on ODP. Autonomous agents andmulti-agent systems,2003,7(3):199-228.
[45]Lind J. Iterative software engineering for multiagent systems: the MASSIVE method. 2001.
[46]Wagner G. The Agent-Object-Relationship metamodel: towards a unified view of state and behavior, Information Systems,2003,28(5):475-504.
[47]Padgham L and Winikoff M. Prometheus: A methodology for developing intelligent agents,Springer-Verlag,2002:174-185.
[48]Bresciani P, Perini A, Giorgini P, Giunchiglia F and Mylopoulos J. Tropos: An agent-oriented software development methodology. Autonomous agents and multi-agent systems,2004,8(3):203-236.
[49]Yu E. Agent-oriented modelling: software versus the world. Agent-Oriented Software Engineering II,2002:206-225.
[50]Brazier F, Dunin-Keplicz B, Jennings N and Treur J. Desire: Modelling multi-agent systems in a compositional formal framework. International Journal of Cooperative Information Systems,1997,6(1): 67.
[51]Rao D. A methodology and modelling technique for systems of BDI agents.Springer Verlag,1996.
[52]Iglesias C, Garijo M, González J and Velasco J. Analysis and design of multiagent systems using MAS-CommonKADS. Intelligent Agents IV Agent Theories, Architectures, and Languages,1998:313-327.
[53]Bush G, Cranefield S and Purvis M. The Styx agent methodology. The Information Science Discussion Paper Series, 2001.
[54]Wooldridge M, Jennings N and Kinny D. The Gaia methodology for agent-oriented analysis and design. Autonomous agents and multi-agent systems,2000,3(3):285-312.
[55]Ferber J and Gutknecht O. A meta-model for the analysis and design of organizations in multi-agent systems.Citeseer,1998.
[56]Yan Q, SHAN L, MAO X and Qi Z. ROMAS: A role-based modeling method for multi-agent system. Proceedings of second international conference on active media technology,2003:156-161.
[57]赵龙文.多Agent系统的组织结构与协同.计算机工程与应用,2000,36(10):59-61.
[58]Weiss G. Multi-agent sysems: a modern approach to distributed artificial intelligence.MIT Press,2000.
[59]Smith R. The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on computers,1980,100(29): 1104-1113.
[60]Finin T, Fritzson R, McKay D and McEntire R. KQML as an agent communication language. ACM,1994.
[61]程显毅,聂文惠,谢军.面向agent开发环境JACK的实践.北京:科学出版社,2009.
[62]Leszczyna R. Evaluation of agent platforms. International Joint Conference on Autonomous Agents,2004.
[63]Nguyen G, Dang T, Hluchy L, Balogh Z, Laclavik M and Budinska I. Agent platform evaluation and comparison. II-SAS, Pellucid EU 5FP IST-2001-34519 RTD, Technicalreport, Jun,2002.
[64]Vrba P. Java-based agent platform evaluation. Holonic and Multi-Agent Systems for Manufacturing,2003:1086-1087.
[65]Arunachalam S, Zalila-Wenkstern R and Steiner R. Environment mediated Multi Agent Simulation Tools–A Comparison. Engineering environment-mdeiated coordination in self-organizing and self-adapting systems ECOSOA,2008,57.
[66]Jennings N,Wooldridge M. Applications of intelligent agents. Agent technology: Foundations, applications and markets,1998:3-28.
[67]Niu G, Han T, Yang B and Tan A. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing,2007,21(3):1285-1299.
[68]Ren X, Thompson H and Fleming P. An agent-based system for distributed fault diagnosis. International Journal of Knowledge-Based and Intelligent Engineering Systems,2006,10(5):319-335.
[69]McArthur S, Davidson E, Hossack J and McDonald J. Automating power system fault diagnosis through multi-agent system technology,2004.
[70]Yang J, Montakhab M, Pipe A and Davies T. Application of multi-agent technology to fault diagnosis of power distribution systems. Proceedings of Engineering of Intelligent Systems,2004.
[71]蔡卫峰.分层分布式电力系统故障诊断.南京理工大学学报:自然科学版,2003,27(4): 442-445.
[72]董海鹰,白建社.基于多Agent联合的变电站故障诊断模型.电力系统及其自动化学报, 2002,14(5):20-24.
[73]左万里.基于多Agent的智能故障诊断系统研究.计算机与现代化,2003,8:4-6.
[74]张全海,叶晨洲,施鹏飞.基于Multi-Agents分布式医学诊断系统研究.信息与控制, 2003,32(1):23-27.
[75]吴伟蔚,吴今培.故障诊断Agent研究. pp. 393-399 (振动工程学报, 2000).
[76]Sahasrabudhe V and Mehra A. A multi-agent control system framework for smart structures. AIAA, 1998.
[77]Weijun W, Xiangju Q and Linliang G. Multi-agent Based Hierarchy Simulation Models of Carrier-based Aircraft Catapult Launch. Chinese Journal of Aeronautics, 2008, 21(3):223-231
[78]李伟,王仲生.基于Multi-agent的飞机智能故障诊断与监控系统设计.计算机测量与控制,2007,15(11):1424-1426.
[79]Shehory O, Sycara K, Sukthankar G and Mukherjee V. Agent aided aircraft maintenance. ACM, 1999,306-312.
[80]刘勇.多Agent系统理论和应用研究.重庆:重庆大学博士论文,2003.
[81]Muscettola N, Nayak P, Pell B and Williams B. Remote agent: To boldly go where no AI system has gone before. Artificial Intelligence,1998,103(1-2):5-47.
[82]Momoh J, Oliver W and R E J. Fault Diagnosis of Power Systems Using Intelligent Systems. 1997.
[83]范显峰,姜兴渭.基于多Agent的卫星故障诊断融合技术研究.中国空间科学技术, 2003, 23(2):39-44.
[84]张晓光,代树武.基于多Agent的航天自主运行系统.计算机工程,2008,34(6):243-245.
[85]邓薇,李家文.液体火箭发动机多Agent故障诊断技术研究.航空动力学报,2008, 23(7): 1341-1345.
[86]Xingwei FXJ. Research of Multi-agent based satellite fault diagnosis and fusiontechnology. Chinese space science and technology,2003,23(2):39-44.
[87]Franco Z, Nicholas R.J, Micchael W.Developing Multiagent Systems: The Gaia Methodology. ACM Transactions on Software Engineering and Methodology, 2003, 12(3):317-370.
[88]王仲生,刘贞报,隆莹. D-S多Agent飞行器结构系统早期故障智能诊断.西北工业大学学报,2006,24(5):600-603.
[89]罗贺.多Agent信息融合与协商及其在故障诊断中的应用研究.合肥:合肥工业大学博士论文,2009.
[90]张正道.复杂非线性系统故障检测与故障预报.南京:南京航空航天大学博士论文,2006.
[89]Jennings N,Wooldridge M. Applications of intelligent agents. Agent technology: Foundations, applications and markets,1998:3-28.
[90]William E.V,Roberts N.H. Fault tree handbook. Nuclear Regulatory Commission,1987.
[91]Lee W, Grosh D. and Tillman F. Fault tree analysis, methods, and applications- a review.IEEE Transactions on Reliability, 1985.
[92]Rauzy A. New algorithms for fault trees analysis. Reliability Engineering & System Safety,1993,40(3):203-211.
[93]Vesely W.E, Dugan J, Fragola J, Minarick J and Railsback J. Fault tree handbook with aerospace applications. NASA,2002.
[94]曲东才,陈琪,张睿智. Elan网络在某型自动驾驶仪飞控盒多故障诊断中的仿真研究.飞机设计,2008,28(2),62-66.
[95]宋华,张洪钺.多故障的奇偶方程—参数估计诊断方法.控制与决策,2003,18(4):413-417.
[96]龙兵,姜兴渭,宋政吉.基于多信号模型航天器多故障诊断技术研究.宇航学报,2004, 25(5):591-594.
[97]周小勇,叶银忠.基于Mallat塔式算法小波变换的多故障诊断方法.控制与决策,2004, 19(5):592-594.
[98]张周锁,李凌均.基于支持向量机的机械故障诊断方法研究.西安交通大学学报,2002, 36(12):1303-1306.
[99]周福娜,文成林,汤天浩,陈志国.基于指定元分析的多故障诊断方法.自动化学报,2009, 35(7):971-982.
[100]张晓莉,王苗,罗文劼.数据结构与算法.北京:机械工业出版社,2008.
[101]Koren I and Krishna C. Fault-Tolerant Systems. Elsevier/Morgan Kaufmann,2007.
[102]Zhang Y and Jiang J. Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control,2008,32(2):229-252.
[103]Kim K S, Lee K J and Kim Y. Reconfigurable flight control system design using direct adaptive method. AIAA,2003:543-550.
[104]Tao G, Chen S and Joshi S M. An adaptive actuator failure compensation controller using outputfeedback,2002:506-511.
[105]Ye D and Yang G. Adaptive fault-tolerant tracking control against actuator faultswith application to flight control. IEEE Transactions on Control Systems Technology, 2006,14(6):1088-1096.
[106]Niksefat N and Sepehri N. A QFT fault-tolerant control for electrohydraulic positioning systems, IEEE Transactions on Control Systems Technology,2002, 10(4):626-632.
[107]Hess R A and Wells S R. Sliding mode control applied to reconfigurable flight control design. Journal of Guidance, Control and Dynamics, 2003,26(3):452-462.
[108]Alwi H and Edwards C. Fault tolerant control using sliding modes with on-line control allocation. Automatica,2008,44(7):1859-1866.
[109]Niemann H. A setup for active fault diagnosis. IEEE Transactions on Automatic Control,2006,51(9):1572-1578.
[110]Mellouli S. FATMAS: A Methodology to Design Fault-tolerant Multi-agent Systems,ph.D dissertation,University of Laval,2005.
[111]Tu F, Pattipati K R, Deb S and Malepati V N. Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems.IEEE Transactions on Systems,Man and Cybernetics,2003,33(1):73-85.
[112]Lin Y, Lu F and Cheng K. Multiple-fault diagnosis based on adaptive diagnostic test pattern generation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2007,26(5):932-942.
[113]刘亚.复杂非线性系统的智能自适应重构控制.南京:南京航空航天大学博士论文,2003.
[114]Noy N and McGuinness D. Ontology development 101: A guide to creating your first ontology,Citeseer, 2001.
[115]杜小勇,李曼,王珊.本体学习研究综述. Journal of Software,2006,17(9):1837-1847.
[116]Uschold M,Gruninger M. Ontologies: Principles, methods and applications. The Knowledge Engineering Review,2009,11(2):93-136.
[117]McGuinness D, Van Harmelen F. OWL web ontology language overview. W3C recommendation, 2004, 10:2004-2003.
[118]Horrocks I, Patel-Schneider P, Boley H, Tabet S, Grosof B and Dean M. SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission, 2004.
[119]O' Connor M, Shankar R, Tu S, Nyulas C, Das A and Musen M. Efficiently querying relational databases using OWL and SWRL. Web Reasoning and Rule Systems: 2007, 361-363.
[120]Laboratories S N. Jess, the Rule Engine for the Java Platform. http://Herzberg.ca.sandia.gov/jess, 2009.
[121]Eslinger R, Chandler P. Self-repairing flight control system program overview. Aerospace and Electronics Conference,1988,2:504-511.
[122]周川,胡维礼,陈庆伟,胡寿松.飞机舵面结构故障检测与重构的模糊观测器方法.信息与控制, 2001.
[123]苏浩秦,宋述杰,邓建华.基于限制最小二乘估计的飞机舵面故障诊断方法.机械科学与技术,2005,24(9):1033-1035.
[124]Neches R, Fikes R, Finin T, Gruber T,Patil R. Enabling technology for knowledge sharing. AI magazine,1991,12(3):36.
[125]Gruber T. A translation approach to portable ontology specifications. Knowledge acquisition, 1993(5):199.
[126]Gruber T. Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human Computer Studies,1995,43(5):907-928.
[127]何永钧.通过实践探索适用于飞机研制的系统工程.航空系统工程,1995(3):20-25.
[128]Willcox K,Wakayama S. Simultaneous optimization of a multiple-aircraft family. Journal of Aircraft,2003,40(4):616-622.
[129]雍明培,余雄庆.基于模块化产品平台的飞机族设计技术探讨.飞机设计,2006(4):30-37.
[130]Fuller J, Aeronautics L and Texas F. The role of manned aircraft in the future. AIAA,2003.
[131]Allison J, Roth B, Kokkolaras M, Kroo I and Papalambros P. Aircraft family design using decomposition-based methods. Citeseer,2006.
[132]党育辉.通用型舰载支援飞机的模块化布局设计概念.飞机工程,2006(2):10-15.
[133]Kan Y, Shou-song H. Modeling of Self-repairing Aircraft Based on Multi-agent System. Proceedings of Fifth International Conference on Natural Computation, 2009,5:241-245.
[134]Blair J, Powers P. Pave Pillar in-house research final report.1992:193-199.
[135]王勇,于宏坤.机载计算机系统.北京:航空工业出版社, 2008.
[136]Morgan D. PAVE PACE: System avionics for the 21st century. IEEE Aerospace and Electronic Systems Magazine,1989,4(1):12-22.
[137]陈明,张京妹.控制系统可靠性设计.西安:西北工业大学出版社, 2006.
[138]李京生.机载传感器发展与展望.航空精密制造技术,2006,42(3):1-4.
[139]Wagner G, Tulba F. Agent-oriented modeling and agent-based simulation. Conceptual Modeling for Novel Application Domains, 2003:205-216.
[140]Smith M,Chawla K,Van D.W.Numerical simulation of a complete STOVL aircraft in ground effect,AIAA,1991: 738-748.
[141]Qi XH, Yang ZJ,Wu XB.Survey study of self-repairing flight control system on UAV, Control Engineering of China,13(6):513-516.
[142]Mendham P, Clarke T. Macsim: A Simulink Enabled Environment for Multi-Agent System Simulation,Proceedings of the 16th IFAC World Congress,2005.
[143]Sonneveldt, L. Nonlinear F-16 model description. Technical report, Delft University of Technology,The Netherlands,2006.
[2] Frank P. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. Automatica, 1990,26(3):459-474.
[3] Ding S. Model-based fault diagnosis techniques: design schemes, algorithms, and tools, Springer Verlag, 2008.
[4] Isermann R. Model-based fault-detection and diagnosis-status and applications. Annual Reviews in control, 2005,29(1):71-85.
[5]胡寿松,周川,王源.基于小波神经网络的组合故障模式识别.自动化学报,2002,28(4):540-543.
[6]余红英,潘宏侠.基于分形的机械故障诊断方法研究.华北工学院学报,2005,26(2): 115-118.
[7]李霁红,康锐,贾颖.复杂系统的模糊故障诊断方法研究.系统工程与电子技术,2005, 27(7):1322-1324.
[8]杨帆,萧德云. SDG建模及其应用的进展.控制理论与应用,2005,22(5):767-774.
[9]李冬辉,刘浩.基于概率神经网络的故障诊断方法及应用.系统工程与电子技术,2004, 26(7):997-999.
[10]李青,史雅琴,周扬.基于案例推理方法在飞机故障诊断中的应用.北京航空航天大学学报,2007,33(5):622-626.
[11]徐波,于劲松,李行善.复杂系统的智能故障诊断.信息与控制,2004,33(1):56-60.
[12]张晓阳.面向复杂系统生命周期的故障诊断技术研究.南京:南京理工大学博士论文,2005.
[13]Russell S, Norvig P. Artificial intelligence: A modern approach. New Jersey: Pearson Education, 1995.
[14]Maes P. Artificial life meets entertainment: lifelike autonomous agents. Communications of the ACM,1995,38(11):108-114.
[15]Smith D, Cypher A and Spohrer J. KidSim: programming agents without a programming language. Communications of the ACM,1994,37(7):54-67.
[16]Hayes-Roth B. An architecture for adaptive intelligent systems. Artificial Intelligence,1995,72(1-2):329-365.
[17]Wooldridge M. and Jennings N. Agent theories, architectures, and languages: a survey. Intelligent agents,1995(890):1-39.
[18]Sycara K. Multiagent systems. AI magazine,1998,19(2):79-92.
[19]Wooldridge M. An introduction to multiagent systems. UK:Wiley,2009.
[20]Wooldridge M and Jennings N. Intelligent agents: Theory and practice. The Knowledge Engineering Review,2009,10(2):115-152.
[21]Gechter F, Chevrier V and Charpillet F. A reactive agent-based problem-solving model: Application to localization and tracking. ACM Transactions on Autonomous and Adaptive Systems (TAAS),2006,1(2):222.
[22]Bonasso R, Firby R, Gat E, Kortenkamp D, Miller D and Slack M. Experiences with an architecture for intelligent, reactive agents. Journal of Experimental &Theoretical Artificial Intelligence,1997,9(2):237-256.
[23]Guessoum Z. A hybrid agent model: a reactive and cognitive behavior. IEEE Computer Society:1997.
[24]史忠植.智能主体及其应用.北京:科学出版社, 2000.
[25]廖守亿.复杂系统基于Agent的建模与仿真方法研究及应用.长沙:国防科学技术大学博士论文,2005.
[26]Jennings N, Sycara K and Wooldridge M. A roadmap of agent research and development. Autonomous agents and multi-agent systems,1998,1(1):7-38.
[27]Wooldridge M. An introduction to multiagent systems. UK:Wiley,2009.
[28]Nwana H. Software agents: An overview. The Knowledge Engineering Review,2009, 11(3): 205-244.
[29]Caglayan A, Harrison C and Harrison C. Agent Source Book–a complete guide to Desktop, Internet and Intranet Agents.UK:Wiley, 1997.
[30]Tumer K and Agogino A. Distributed agent-based air traffic flow management. ACM, 2007.
[31]张欧亚,佟明安,马瑞萍,温孚禄.人机一体化的巡航导弹监控Agent设计.系统工程理论与实践,2007,27(9):159-164.
[32]Baxter J, Horn G and Leivers D. Fly-by-agent: Controlling a pool of UAVs via a multi-agent system. Knowledge-Based Systems,2008,21(3):232-237.
[33]宗令蓓,谢凡,秦世引.基于MAS的无人机编队飞行智能优化控制.航空学报,2008, 29(5):1326-1333.
[34]Ota J. Multi-agent robot systems as distributed autonomous systems. Advanced engineering informatics,2006,20(1):59-70.
[35]Ming Z, Jianwen R, Gengyin L, Xiang-hai X, Zhi-xu C, Jing-huai L and Gui-zhong Y. A Multi-Agent based dispatching operation instructing system in electric power systems.Proceedings of Chinese Society of Electrical Engineering,2004, 24(4):58-62.
[36]Viamonte M, Ramos C, Rodrigues F and Cardoso J. ISEM: a multiagent Simulator for testing agent market strategies. IEEE Transactions on Systems, Man and Cybernetics-Part C: applications and reviews,2006,36(1):107.
[37]Caridi M and Cavalieri S. Multi-agent systems in production planning and control: an overview. Production Planning & Control,2004,15(2):106-118.
[38]Klusch M. Information agent technology for the internet: A survey. Data & Knowledge Engineering,2001,36(3):337-372.
[39]Van Dyke Parunak H, Savit R and Riolo R. Agent-based modeling vs. equation-based modeling: A case study and users' guide. Springer,1998: 10-25.
[40]Bonabeau E. Agent-based modeling: Methods and techniques for simulating human systems.National Acad Sciences,2002:7280.
[41]Huhns M and Stephens L. Multiagent Systems and Societies of Agents,2008.
[42]毛新军.面向主体的软件开发.北京:清华大学出版社,2005.
[43]DeLoach S. Analysis and Design using MaSE and agentTool.Citeseer,2001.
[44]Gervais M. ODAC: An agent-oriented methodology based on ODP. Autonomous agents andmulti-agent systems,2003,7(3):199-228.
[45]Lind J. Iterative software engineering for multiagent systems: the MASSIVE method. 2001.
[46]Wagner G. The Agent-Object-Relationship metamodel: towards a unified view of state and behavior, Information Systems,2003,28(5):475-504.
[47]Padgham L and Winikoff M. Prometheus: A methodology for developing intelligent agents,Springer-Verlag,2002:174-185.
[48]Bresciani P, Perini A, Giorgini P, Giunchiglia F and Mylopoulos J. Tropos: An agent-oriented software development methodology. Autonomous agents and multi-agent systems,2004,8(3):203-236.
[49]Yu E. Agent-oriented modelling: software versus the world. Agent-Oriented Software Engineering II,2002:206-225.
[50]Brazier F, Dunin-Keplicz B, Jennings N and Treur J. Desire: Modelling multi-agent systems in a compositional formal framework. International Journal of Cooperative Information Systems,1997,6(1): 67.
[51]Rao D. A methodology and modelling technique for systems of BDI agents.Springer Verlag,1996.
[52]Iglesias C, Garijo M, González J and Velasco J. Analysis and design of multiagent systems using MAS-CommonKADS. Intelligent Agents IV Agent Theories, Architectures, and Languages,1998:313-327.
[53]Bush G, Cranefield S and Purvis M. The Styx agent methodology. The Information Science Discussion Paper Series, 2001.
[54]Wooldridge M, Jennings N and Kinny D. The Gaia methodology for agent-oriented analysis and design. Autonomous agents and multi-agent systems,2000,3(3):285-312.
[55]Ferber J and Gutknecht O. A meta-model for the analysis and design of organizations in multi-agent systems.Citeseer,1998.
[56]Yan Q, SHAN L, MAO X and Qi Z. ROMAS: A role-based modeling method for multi-agent system. Proceedings of second international conference on active media technology,2003:156-161.
[57]赵龙文.多Agent系统的组织结构与协同.计算机工程与应用,2000,36(10):59-61.
[58]Weiss G. Multi-agent sysems: a modern approach to distributed artificial intelligence.MIT Press,2000.
[59]Smith R. The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on computers,1980,100(29): 1104-1113.
[60]Finin T, Fritzson R, McKay D and McEntire R. KQML as an agent communication language. ACM,1994.
[61]程显毅,聂文惠,谢军.面向agent开发环境JACK的实践.北京:科学出版社,2009.
[62]Leszczyna R. Evaluation of agent platforms. International Joint Conference on Autonomous Agents,2004.
[63]Nguyen G, Dang T, Hluchy L, Balogh Z, Laclavik M and Budinska I. Agent platform evaluation and comparison. II-SAS, Pellucid EU 5FP IST-2001-34519 RTD, Technicalreport, Jun,2002.
[64]Vrba P. Java-based agent platform evaluation. Holonic and Multi-Agent Systems for Manufacturing,2003:1086-1087.
[65]Arunachalam S, Zalila-Wenkstern R and Steiner R. Environment mediated Multi Agent Simulation Tools–A Comparison. Engineering environment-mdeiated coordination in self-organizing and self-adapting systems ECOSOA,2008,57.
[66]Jennings N,Wooldridge M. Applications of intelligent agents. Agent technology: Foundations, applications and markets,1998:3-28.
[67]Niu G, Han T, Yang B and Tan A. Multi-agent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing,2007,21(3):1285-1299.
[68]Ren X, Thompson H and Fleming P. An agent-based system for distributed fault diagnosis. International Journal of Knowledge-Based and Intelligent Engineering Systems,2006,10(5):319-335.
[69]McArthur S, Davidson E, Hossack J and McDonald J. Automating power system fault diagnosis through multi-agent system technology,2004.
[70]Yang J, Montakhab M, Pipe A and Davies T. Application of multi-agent technology to fault diagnosis of power distribution systems. Proceedings of Engineering of Intelligent Systems,2004.
[71]蔡卫峰.分层分布式电力系统故障诊断.南京理工大学学报:自然科学版,2003,27(4): 442-445.
[72]董海鹰,白建社.基于多Agent联合的变电站故障诊断模型.电力系统及其自动化学报, 2002,14(5):20-24.
[73]左万里.基于多Agent的智能故障诊断系统研究.计算机与现代化,2003,8:4-6.
[74]张全海,叶晨洲,施鹏飞.基于Multi-Agents分布式医学诊断系统研究.信息与控制, 2003,32(1):23-27.
[75]吴伟蔚,吴今培.故障诊断Agent研究. pp. 393-399 (振动工程学报, 2000).
[76]Sahasrabudhe V and Mehra A. A multi-agent control system framework for smart structures. AIAA, 1998.
[77]Weijun W, Xiangju Q and Linliang G. Multi-agent Based Hierarchy Simulation Models of Carrier-based Aircraft Catapult Launch. Chinese Journal of Aeronautics, 2008, 21(3):223-231
[78]李伟,王仲生.基于Multi-agent的飞机智能故障诊断与监控系统设计.计算机测量与控制,2007,15(11):1424-1426.
[79]Shehory O, Sycara K, Sukthankar G and Mukherjee V. Agent aided aircraft maintenance. ACM, 1999,306-312.
[80]刘勇.多Agent系统理论和应用研究.重庆:重庆大学博士论文,2003.
[81]Muscettola N, Nayak P, Pell B and Williams B. Remote agent: To boldly go where no AI system has gone before. Artificial Intelligence,1998,103(1-2):5-47.
[82]Momoh J, Oliver W and R E J. Fault Diagnosis of Power Systems Using Intelligent Systems. 1997.
[83]范显峰,姜兴渭.基于多Agent的卫星故障诊断融合技术研究.中国空间科学技术, 2003, 23(2):39-44.
[84]张晓光,代树武.基于多Agent的航天自主运行系统.计算机工程,2008,34(6):243-245.
[85]邓薇,李家文.液体火箭发动机多Agent故障诊断技术研究.航空动力学报,2008, 23(7): 1341-1345.
[86]Xingwei FXJ. Research of Multi-agent based satellite fault diagnosis and fusiontechnology. Chinese space science and technology,2003,23(2):39-44.
[87]Franco Z, Nicholas R.J, Micchael W.Developing Multiagent Systems: The Gaia Methodology. ACM Transactions on Software Engineering and Methodology, 2003, 12(3):317-370.
[88]王仲生,刘贞报,隆莹. D-S多Agent飞行器结构系统早期故障智能诊断.西北工业大学学报,2006,24(5):600-603.
[89]罗贺.多Agent信息融合与协商及其在故障诊断中的应用研究.合肥:合肥工业大学博士论文,2009.
[90]张正道.复杂非线性系统故障检测与故障预报.南京:南京航空航天大学博士论文,2006.
[89]Jennings N,Wooldridge M. Applications of intelligent agents. Agent technology: Foundations, applications and markets,1998:3-28.
[90]William E.V,Roberts N.H. Fault tree handbook. Nuclear Regulatory Commission,1987.
[91]Lee W, Grosh D. and Tillman F. Fault tree analysis, methods, and applications- a review.IEEE Transactions on Reliability, 1985.
[92]Rauzy A. New algorithms for fault trees analysis. Reliability Engineering & System Safety,1993,40(3):203-211.
[93]Vesely W.E, Dugan J, Fragola J, Minarick J and Railsback J. Fault tree handbook with aerospace applications. NASA,2002.
[94]曲东才,陈琪,张睿智. Elan网络在某型自动驾驶仪飞控盒多故障诊断中的仿真研究.飞机设计,2008,28(2),62-66.
[95]宋华,张洪钺.多故障的奇偶方程—参数估计诊断方法.控制与决策,2003,18(4):413-417.
[96]龙兵,姜兴渭,宋政吉.基于多信号模型航天器多故障诊断技术研究.宇航学报,2004, 25(5):591-594.
[97]周小勇,叶银忠.基于Mallat塔式算法小波变换的多故障诊断方法.控制与决策,2004, 19(5):592-594.
[98]张周锁,李凌均.基于支持向量机的机械故障诊断方法研究.西安交通大学学报,2002, 36(12):1303-1306.
[99]周福娜,文成林,汤天浩,陈志国.基于指定元分析的多故障诊断方法.自动化学报,2009, 35(7):971-982.
[100]张晓莉,王苗,罗文劼.数据结构与算法.北京:机械工业出版社,2008.
[101]Koren I and Krishna C. Fault-Tolerant Systems. Elsevier/Morgan Kaufmann,2007.
[102]Zhang Y and Jiang J. Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control,2008,32(2):229-252.
[103]Kim K S, Lee K J and Kim Y. Reconfigurable flight control system design using direct adaptive method. AIAA,2003:543-550.
[104]Tao G, Chen S and Joshi S M. An adaptive actuator failure compensation controller using outputfeedback,2002:506-511.
[105]Ye D and Yang G. Adaptive fault-tolerant tracking control against actuator faultswith application to flight control. IEEE Transactions on Control Systems Technology, 2006,14(6):1088-1096.
[106]Niksefat N and Sepehri N. A QFT fault-tolerant control for electrohydraulic positioning systems, IEEE Transactions on Control Systems Technology,2002, 10(4):626-632.
[107]Hess R A and Wells S R. Sliding mode control applied to reconfigurable flight control design. Journal of Guidance, Control and Dynamics, 2003,26(3):452-462.
[108]Alwi H and Edwards C. Fault tolerant control using sliding modes with on-line control allocation. Automatica,2008,44(7):1859-1866.
[109]Niemann H. A setup for active fault diagnosis. IEEE Transactions on Automatic Control,2006,51(9):1572-1578.
[110]Mellouli S. FATMAS: A Methodology to Design Fault-tolerant Multi-agent Systems,ph.D dissertation,University of Laval,2005.
[111]Tu F, Pattipati K R, Deb S and Malepati V N. Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems.IEEE Transactions on Systems,Man and Cybernetics,2003,33(1):73-85.
[112]Lin Y, Lu F and Cheng K. Multiple-fault diagnosis based on adaptive diagnostic test pattern generation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2007,26(5):932-942.
[113]刘亚.复杂非线性系统的智能自适应重构控制.南京:南京航空航天大学博士论文,2003.
[114]Noy N and McGuinness D. Ontology development 101: A guide to creating your first ontology,Citeseer, 2001.
[115]杜小勇,李曼,王珊.本体学习研究综述. Journal of Software,2006,17(9):1837-1847.
[116]Uschold M,Gruninger M. Ontologies: Principles, methods and applications. The Knowledge Engineering Review,2009,11(2):93-136.
[117]McGuinness D, Van Harmelen F. OWL web ontology language overview. W3C recommendation, 2004, 10:2004-2003.
[118]Horrocks I, Patel-Schneider P, Boley H, Tabet S, Grosof B and Dean M. SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission, 2004.
[119]O' Connor M, Shankar R, Tu S, Nyulas C, Das A and Musen M. Efficiently querying relational databases using OWL and SWRL. Web Reasoning and Rule Systems: 2007, 361-363.
[120]Laboratories S N. Jess, the Rule Engine for the Java Platform. http://Herzberg.ca.sandia.gov/jess, 2009.
[121]Eslinger R, Chandler P. Self-repairing flight control system program overview. Aerospace and Electronics Conference,1988,2:504-511.
[122]周川,胡维礼,陈庆伟,胡寿松.飞机舵面结构故障检测与重构的模糊观测器方法.信息与控制, 2001.
[123]苏浩秦,宋述杰,邓建华.基于限制最小二乘估计的飞机舵面故障诊断方法.机械科学与技术,2005,24(9):1033-1035.
[124]Neches R, Fikes R, Finin T, Gruber T,Patil R. Enabling technology for knowledge sharing. AI magazine,1991,12(3):36.
[125]Gruber T. A translation approach to portable ontology specifications. Knowledge acquisition, 1993(5):199.
[126]Gruber T. Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human Computer Studies,1995,43(5):907-928.
[127]何永钧.通过实践探索适用于飞机研制的系统工程.航空系统工程,1995(3):20-25.
[128]Willcox K,Wakayama S. Simultaneous optimization of a multiple-aircraft family. Journal of Aircraft,2003,40(4):616-622.
[129]雍明培,余雄庆.基于模块化产品平台的飞机族设计技术探讨.飞机设计,2006(4):30-37.
[130]Fuller J, Aeronautics L and Texas F. The role of manned aircraft in the future. AIAA,2003.
[131]Allison J, Roth B, Kokkolaras M, Kroo I and Papalambros P. Aircraft family design using decomposition-based methods. Citeseer,2006.
[132]党育辉.通用型舰载支援飞机的模块化布局设计概念.飞机工程,2006(2):10-15.
[133]Kan Y, Shou-song H. Modeling of Self-repairing Aircraft Based on Multi-agent System. Proceedings of Fifth International Conference on Natural Computation, 2009,5:241-245.
[134]Blair J, Powers P. Pave Pillar in-house research final report.1992:193-199.
[135]王勇,于宏坤.机载计算机系统.北京:航空工业出版社, 2008.
[136]Morgan D. PAVE PACE: System avionics for the 21st century. IEEE Aerospace and Electronic Systems Magazine,1989,4(1):12-22.
[137]陈明,张京妹.控制系统可靠性设计.西安:西北工业大学出版社, 2006.
[138]李京生.机载传感器发展与展望.航空精密制造技术,2006,42(3):1-4.
[139]Wagner G, Tulba F. Agent-oriented modeling and agent-based simulation. Conceptual Modeling for Novel Application Domains, 2003:205-216.
[140]Smith M,Chawla K,Van D.W.Numerical simulation of a complete STOVL aircraft in ground effect,AIAA,1991: 738-748.
[141]Qi XH, Yang ZJ,Wu XB.Survey study of self-repairing flight control system on UAV, Control Engineering of China,13(6):513-516.
[142]Mendham P, Clarke T. Macsim: A Simulink Enabled Environment for Multi-Agent System Simulation,Proceedings of the 16th IFAC World Congress,2005.
[143]Sonneveldt, L. Nonlinear F-16 model description. Technical report, Delft University of Technology,The Netherlands,2006.
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