民用飞机故障诊断与故障风险评估的TMSDG方法研究
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摘要
随着国内民机产业的飞速发展，飞机故障诊断和故障风险评估是亟待解决的问题。对于民用飞机尤其是新研制飞机的各种疑难故障,由于其引发因素繁多且相互耦合,故障征兆与最终故障原因之间无明确对应关系，因而在诊断过程中，存在对象的精确定量模型难以建立，同时先验知识又匮乏的问题。本文以飞机多工况系统为研究对象，提出了一种新的定性描述模型—过程动态有向图TMSDG (Test-Maintenance Signed Directed Graph)。一方面充分利用符号有向图(signed directedGraph，sDG)模型包容深层知识的能力和处理复杂因果关系独特的优势，另外一方面弥补SDG在诊断诊断和维修领域的知识表示和处理方面的不足，突破SDG实际应用的局限性，从而为复杂系统的故障诊断与故障风险分析提供有效支持。论文主要研究四个问题：
(1)给出了TMSDG模型的定义。TMSDG模型将SDG模型覆盖在系统结构模型上，增加描述测试与状态参数的关联关系，进一步引入了工况条件。TMSDG模型不但继承了SDG模型的特性，更重要的是包容结构、状态、测试、维修更多潜在信息,因而不仅具有自动诊断和而且还为交互诊断能力提供了支持，并满足多工况过程动态系统模型随工况变化调整的实际需求。根据复杂系统故障在层间传播的特点，讨论了一种基于分层思想的建模方法，降低系统级TMSDG模型建模的难度，使模型具有良好的封装性、可重用性，也使模型具备分层诊断能力，适应复杂系统诊断和民用航空分级维修体制。
(2)研究了TMSDG“分而治之”的分层诊断策略。基于TMSDG的诊断推理，首先确定发生故障的具体工况，重构单稳定工况SDG模型。在单稳定工况诊断过程中引入故障--故障关联矩阵，使用故障--故障关联矩阵和可达性理论扩充了相容性理论，进而计算可能的故障源集合。提出的TMSDG分层诊断策略减小故障源搜索空间的大小，避免了未测节点的符号假设、停止假设的判断和修正假设符号的过程，大大提高了诊断算法的效率和诊断结果的准确性。最后，TMSDG诊断策略综合多个工况诊断结果，进一步减少冗余解，提高诊断精度。
(3)提出了一种多值AND/OR图描述飞机系统测试序列问题，并研究了基于最大单位信息熵故障诊断树的交互式诊断算法，充分利用了故障概率信息、测试结果及测试费用，得到了具有最大增益测试费用比的测试序列。该算法不用列举整个结果树的集合，运算中不存在反馈，计算结果用多值AND/OR描述,可有效减少计算复杂度和平均测试代价,而且生成的故障诊断策略能有效、快速地指导维修人员进行故障隔离。提出的方法能自适应不同维修级别不同的故障隔离精度要求，生成相应的测试序列。
(4)建立了基于灰色聚类方法的故障风险评估方法。对于已识别的故障模式,通过TMSDG模型获取故障因果链和故障树，进行故障劣化趋势预测,明确故障模式的发展变化过程，以故障因果链为分析对象实现整体和部分相结合的故障风险评估。该方法突破了以往FMEA方法的局限，解决了风险评估过程中的不确定性和不一致性问题，从而更为准确地评估故障风险，其分析结果不仅能将故障模式按照风险顺序排列，还详细阐述了具有实用意义的中间过程，能更有效地帮助决策者分析制定维修策略及改进措施，增强在整个寿命周期中由阶段消除、控制这些危险的能力。
通过理论分析和气源系统诊断系统实例研究表明，基于TMSDG的故障诊断方法能有效地应用于民用飞机多过程故障诊断。TMSDG的故障诊断方法通用性良好，论文研究结果对于复杂系统的故障诊断与故障风险分析具有重要的参考意义。
When fault diagnosis is conducted in system of Civil aircraft，especially in the newlydeveloped aircraft，There are many complex faults with multiple coupling influencing factors, andsymptoms. It is difficult to build accurate quantitative models and lack of prior knowledge.Tosolve these problems and conduct effective fault isolation of ircraft multi-condition process thispaper proposed A new qualitative description model—process dynamic signed directed graph(TMSDG) based on SDG which has the ability to accommodate large-scale deep knowledge andhandle complicated cause-effect relationship, Four Problem are studied as below:
(1)The definition of TMSDG model is given. TMSDG model is built by combining SDGmodel to the system structure, introducing the stage conditions, unmeasured nodes in SDG.TMSDG not only inherits the characteristics of SDG model but also has the capacity of containinglarge-scale potential information such as structure, test and maintenance. which can support bothautomatic diagnosis and interactive diagnosis.TMSDG can meet the actual needs of change ofoperation conditions. According the characteristics of transmission between hierarchies of failuremodes a hierarchical modeling method which can make modeling easier is studied. the TMSDGmodel has good encapsulation, reusability, so it can support complex system diagnosis and civilaviation maintenance system
(2)A hierarchical diagnostic strategy based on TMSDG is studied．First, the specificconditions of failure are determined and the corresponding single-state stable SDG models arereconstructed. Then, possible fault sources are reasoned based on single-state stable SDG modelby introducing the fault-fault dependence matrix, with accessibility theory and the compatibilitycalculation principle. Last,The final diagnosis algorithm integrated multiple modes of thediagnosis. Hierarchical diagnostic strategy avoids the symbols assumption and symbols correctionof unmeasurement nodes, greatly improving the efficiency of diagnosis and diagnosis accuracy.The further reduce redundancy solution, and to improve the diagnostic accuracy.
(3)A multi-value AND/OR graph is presented to describe the test sequencing problem ofaircraft system and a diagnosis tree generation algorithm based on maximum unit informationentropy is studied. In the given algorithm,the complicated status association and fault-testdependency of complex system fault are modeled and multi-value AND/OR graph with maximuminformation gain per unit cost of the test is developed considering fault probability and testing cost.The AND/OR graph is a fault diagnosis strategy which can effectively help maintenance man tolocate the fault at lower maintenance costs. The generated diagnostic strategy ensures faultisolation with desired precision at desired maintenance level，which meets the requirements ofcivil aircraft maintenance system．
(4)A fault risk assessment algorithm based on gray clustering is proposed．For theidentified failure modes, the deterioration trend are predicted by isolating the event tree and faulttree from TMSDG model. This method improves accuracy of risk valuation since the event treesare regarded as risk evaluation objects. It solves the uncertainties problem and inconsistenciesproblem in traditional FMEA risk assessment. The assessment results includes not only riskpriority of fault mode, but also more practical detailed analysis, which can help to set upmaintenance strategy, improvement measures and enhance the ability of eliminating or control ingthese dangerous in the whole life cycle.
Finally, Based on theoretical analysis，an intelligent Fault diagnosis system is developed andan aircraft bleed system is employed as an object to validate the proposed method. The case resultsproves the effectivity and utility of this model.
(1)给出了TMSDG模型的定义。TMSDG模型将SDG模型覆盖在系统结构模型上，增加描述测试与状态参数的关联关系，进一步引入了工况条件。TMSDG模型不但继承了SDG模型的特性，更重要的是包容结构、状态、测试、维修更多潜在信息,因而不仅具有自动诊断和而且还为交互诊断能力提供了支持，并满足多工况过程动态系统模型随工况变化调整的实际需求。根据复杂系统故障在层间传播的特点，讨论了一种基于分层思想的建模方法，降低系统级TMSDG模型建模的难度，使模型具有良好的封装性、可重用性，也使模型具备分层诊断能力，适应复杂系统诊断和民用航空分级维修体制。
(2)研究了TMSDG“分而治之”的分层诊断策略。基于TMSDG的诊断推理，首先确定发生故障的具体工况，重构单稳定工况SDG模型。在单稳定工况诊断过程中引入故障--故障关联矩阵，使用故障--故障关联矩阵和可达性理论扩充了相容性理论，进而计算可能的故障源集合。提出的TMSDG分层诊断策略减小故障源搜索空间的大小，避免了未测节点的符号假设、停止假设的判断和修正假设符号的过程，大大提高了诊断算法的效率和诊断结果的准确性。最后，TMSDG诊断策略综合多个工况诊断结果，进一步减少冗余解，提高诊断精度。
(3)提出了一种多值AND/OR图描述飞机系统测试序列问题，并研究了基于最大单位信息熵故障诊断树的交互式诊断算法，充分利用了故障概率信息、测试结果及测试费用，得到了具有最大增益测试费用比的测试序列。该算法不用列举整个结果树的集合，运算中不存在反馈，计算结果用多值AND/OR描述,可有效减少计算复杂度和平均测试代价,而且生成的故障诊断策略能有效、快速地指导维修人员进行故障隔离。提出的方法能自适应不同维修级别不同的故障隔离精度要求，生成相应的测试序列。
(4)建立了基于灰色聚类方法的故障风险评估方法。对于已识别的故障模式,通过TMSDG模型获取故障因果链和故障树，进行故障劣化趋势预测,明确故障模式的发展变化过程，以故障因果链为分析对象实现整体和部分相结合的故障风险评估。该方法突破了以往FMEA方法的局限，解决了风险评估过程中的不确定性和不一致性问题，从而更为准确地评估故障风险，其分析结果不仅能将故障模式按照风险顺序排列，还详细阐述了具有实用意义的中间过程，能更有效地帮助决策者分析制定维修策略及改进措施，增强在整个寿命周期中由阶段消除、控制这些危险的能力。
通过理论分析和气源系统诊断系统实例研究表明，基于TMSDG的故障诊断方法能有效地应用于民用飞机多过程故障诊断。TMSDG的故障诊断方法通用性良好，论文研究结果对于复杂系统的故障诊断与故障风险分析具有重要的参考意义。
When fault diagnosis is conducted in system of Civil aircraft，especially in the newlydeveloped aircraft，There are many complex faults with multiple coupling influencing factors, andsymptoms. It is difficult to build accurate quantitative models and lack of prior knowledge.Tosolve these problems and conduct effective fault isolation of ircraft multi-condition process thispaper proposed A new qualitative description model—process dynamic signed directed graph(TMSDG) based on SDG which has the ability to accommodate large-scale deep knowledge andhandle complicated cause-effect relationship, Four Problem are studied as below:
(1)The definition of TMSDG model is given. TMSDG model is built by combining SDGmodel to the system structure, introducing the stage conditions, unmeasured nodes in SDG.TMSDG not only inherits the characteristics of SDG model but also has the capacity of containinglarge-scale potential information such as structure, test and maintenance. which can support bothautomatic diagnosis and interactive diagnosis.TMSDG can meet the actual needs of change ofoperation conditions. According the characteristics of transmission between hierarchies of failuremodes a hierarchical modeling method which can make modeling easier is studied. the TMSDGmodel has good encapsulation, reusability, so it can support complex system diagnosis and civilaviation maintenance system
(2)A hierarchical diagnostic strategy based on TMSDG is studied．First, the specificconditions of failure are determined and the corresponding single-state stable SDG models arereconstructed. Then, possible fault sources are reasoned based on single-state stable SDG modelby introducing the fault-fault dependence matrix, with accessibility theory and the compatibilitycalculation principle. Last,The final diagnosis algorithm integrated multiple modes of thediagnosis. Hierarchical diagnostic strategy avoids the symbols assumption and symbols correctionof unmeasurement nodes, greatly improving the efficiency of diagnosis and diagnosis accuracy.The further reduce redundancy solution, and to improve the diagnostic accuracy.
(3)A multi-value AND/OR graph is presented to describe the test sequencing problem ofaircraft system and a diagnosis tree generation algorithm based on maximum unit informationentropy is studied. In the given algorithm,the complicated status association and fault-testdependency of complex system fault are modeled and multi-value AND/OR graph with maximuminformation gain per unit cost of the test is developed considering fault probability and testing cost.The AND/OR graph is a fault diagnosis strategy which can effectively help maintenance man tolocate the fault at lower maintenance costs. The generated diagnostic strategy ensures faultisolation with desired precision at desired maintenance level，which meets the requirements ofcivil aircraft maintenance system．
(4)A fault risk assessment algorithm based on gray clustering is proposed．For theidentified failure modes, the deterioration trend are predicted by isolating the event tree and faulttree from TMSDG model. This method improves accuracy of risk valuation since the event treesare regarded as risk evaluation objects. It solves the uncertainties problem and inconsistenciesproblem in traditional FMEA risk assessment. The assessment results includes not only riskpriority of fault mode, but also more practical detailed analysis, which can help to set upmaintenance strategy, improvement measures and enhance the ability of eliminating or control ingthese dangerous in the whole life cycle.
Finally, Based on theoretical analysis，an intelligent Fault diagnosis system is developed andan aircraft bleed system is employed as an object to validate the proposed method. The case resultsproves the effectivity and utility of this model.
引文
[1]夏虹.设备故障诊断技术.北京:哈尔滨工业大学出版社,北京航空航天大学出版社2010.
[2]吴海桥.现代大型客机故障诊断专家系统的研究与开发[D].[博士论文],南京:南京航空航天大学,2002.
[3]张萍,王桂增,周东华.动态系统的故障诊断方法[J].控制理论与应用,2000,17(2):153-158.
[4]窦金生,汤天浩.基于知识的故障诊断技术及其在船舶上的应用[J].船舶工程,2007,29(4):27,72-74.
[5]周东华,叶银忠.现代故障诊断与容错控制[M].北京:清华大学出版社,2000.
[6]张明川.人工神经网络技术在民用涡扇发动机故障诊断中的应用.航空工程与维修,2000,(2):29-31.
[7]蒋东翔,王风雨,周明,等.模糊自组织神经网络在航空发动机故障诊断中的应用.航空动力学报,2001,16(1):80-82.
[8] Venkat V.Raghunathan R.Kewen Y.et al. A review of process fault detection and diagnosis PartⅠ:quantitative model-based methods. Computers and Chemical Engineering,2003,27(3):293-311.
[9] Venkat V.Raghunathan R.Kewen Y.et al. A review of process fault detection and diagnosis PartⅡ:qualitative models and search strategies. Computers and Chemical Engineering,2003,27(3):313-326.
[10] Venkat V.Raghunathan R.Kewen Y.et al. A review of process fault detection and diagnosis PartⅢ:process history based methods. Computers and Chemical Engineering,2003,27(3):327-346.
[11] Kuiper B.J. Qualitative Reasoning-Modeling&Simulation with Incomplete Knowledge. Cam-bridge: MIT Press,1994.
[12] Frank P M. Analytical and qualitative model-based fault diagnosis-A survey and some newresults [J]. European Journal of Control,1996,2(1):6-28.
[13] Vedam H. Venkatasubramanian V. Signed Digraph Based Multiple Fault Diagnosis ComputersChem. Engng,1997,21:655-660.
[14]杜殿林,张光红,吴重光.基于知识故障诊断系统所用的深层知识及SDG方法[J].化工自动化及仪表,2005,32(4):8-10.
[15]吴重光.基于符号定向图(SDG)深层知识模型的定性仿真[J].系统仿真学报,2003,(10):351-1355.
[16]王文辉,周东华.基于定性和半定性方法的故障检测与诊断技术[J].控制理论与应用,2002,19(5):653-659.
[17]高伟,邢琰,王南华.基于定性模型的故障诊断方法[J].空间控制技术与应用,2009,35(1):25-29.
[18] de Kleer J, Brown J. A qualitative physics based on confluence[J]. Artificial Intelli-gence,1984,24(1/3):7-83.
[19] Kuipers B. Qualitative simulation [J].Artificial Intelligence,1986,29(3):289-388.
[20] Forbus K. Qualitative process theory[J].Artificial Intelligence,1984,24(1/3):85-168.
[21] Iwasaki Y, Simon H A. Causality in device behavior[J].Artificial Intelligence,1991,51(1/3):11-38.
[22]孙天.红外测温技术在发动机故障诊断中的应用[J].工程机械,2006(12):60-61.
[23]黄选红.孔探检测在航空发动机维护中的应用[J].机械,2009,(8):66-69.
[24]杨善水,刘祥,严仰光.飞机电源系统BIT技术的应用及发展[J].南京航空航天大学学报,1998,30(5):593-596.
[25]石山等.飞机机电BIT技术[M].北京:国防工业出版社,2010.
[26]常夏.电子中央监控系统(ECAM系统)计算机仿真:[硕士学位论文].西安:西安工业大学,2009.
[27]顾铮.空中客车公司的数字化维修技术[J].中国民航学院学报,2005,23(4):26-28.
[28]张加圣,王海涛.飞机维修系统的演变[J].航空工程与维修,2007,(2):25-26.
[29]马磊.国外先进的飞行器故障诊断工具[J].质量与可靠性,2009(5):50-54.
[30] SOMNATH Deb,KRISHHA R Pattipati,VIJAY Raghavan,et al. Multi-signal flowgraphs:a novel approach for system testability analysis and fault diagnosis[C]//1994IEEE Autotestcon Proceedings IEEE,Anaheim,1994:361-373.
[31] SOMNATH Deb,KRISHHA R Pattipati,ROSHAN shrestha.QSI’s integrated diagno-setics toolset[C]//1997IEEE Autotestcon Proceedings IEEE,Anaheim,1997:408-421.
[32] Somnath Deb,Sudipto Ghoshal,Amit Mathur,etal. Multi-signal modeling for diagno-sis, FMECA,and reliability[C]//Proceedings of the IEEE International Conferenceon Systems,Man,and Cybernetics. IEEE,San Diego,1998:3026-3031.
[33]刘松风,朱明初,叶晓慧.基于多信号模型的故障诊断算法研究[J].计算机测量与控制,2009,17(6):1042-1044.
[34] Pattipati K R Raghavan V,Shakeri M,et a1.TEAMS:Testability Engineering AndMaintenance System[C].Invited paper,1994:1989-1996.
[35] Mathur,S.Deb and K.R. Pattipati.Modeling and Real-time Diagnosis、with TEAMSRT. American Control Conference,Philadelphia, June1998.
[36] Deb S,Pattipati K R,Shrestha R.QSI’S Integrated Toolset[C].Proceeding of the IEEEAUTOTESTCON,1997:408-421.
[37]孔令宽.基于多信号模型的卫星故障诊断技术[硕士学位论文],长沙:研究国防科学技术大学,2009.
[38] KleerJ, WilliamsB C. Diagnosing multiple faults[J].Artificial Intelligence,1987,32:97-130.
[39] Sandra C.Hayden,AdamJ.Swee,Scott E.Christa. Livingstone Model-Based Diagnosisof Earth Observing One[C]. AIAA1st Intelligent Systems Technical Conference,Chicago, Illi-nois,2004.
[40] Siamak T, Sun X H. Inference techniques for diagnosisbased on set operations[C].AIAA the1st Intelligent Systems Technical Conference, Chicago, USA, Sep20-22,2004.
[41]代树武.航天器自主运行关键技术的研究[D].中国科学院博士后学位论文,2002.
[42] Narasimhan,S.,Dearden,R.,Benazera,E.Combining Particle Filter sandConsistency-Based Approaches for Monitoring and Diagnosis of Stochastic HybridSystems[C].15th Inter-national Workshop on Principles of Diagnosis(DX04), Carca-ssonne,France,2004.
[43] Steven A.Soots,Bradley T.Burchett. Dynamic Fuzzy Models of the Fastrac StartupSequence for Fault Detection[C].46th AIAA Aerospace Sciences Meeting and Ex-hibit,Reno,Neva-da,2008.
[44] Karsai,G.,Sztipanovits,J.,Ledeczi,A.,etal. Model-Integrated Development of Em-bedded Software [J].Proceedings of the IEEE,2003,91(1):145-164.
[45] Bradley T. Burchett. Discrete Time Linear Models of the Fastrac Startup Sequencefor Fault Detection[C].46thAIAA Aerospace Sciences Meeting and Exhibit, Reno,Nevada,2008.
[46]龙兵.多信号建模与故障诊断方法及其在航天器中的应用研究,[D].[博士学位论文],哈尔滨:哈尔滨工业大学,2005.
[47]张鹏,王婷婷,俞利明.改进的模糊Petri网在民航飞机故障诊断中的应用[J].测控技术,2011,30(4):70-73.
[48]王修岩,薛斌斌,李宗帅.基于Petri网的飞机交流发电机故障诊断系统研究[J].中国民航大学学报,2012.
[49]徐新海.基于层次SDG的航空发动机故障诊断方法研究[硕士学位论文].南京:南京航空航天大学,2010.
[50]马存宝,周方旺,周建民,黄爱军.基于有向序图模型的飞机燃油系统故障诊断的系统设计与实现[J].测控技术,2011,30(11):110-113.
[51]王勉宇.基于图论的故障诊断研究[D].[硕士学位论文］,杭州:浙江大学,2002.
[52]代树武.航天器自主运行关键技术的研究[D].中国科学院博士后学位论文,2002.
[53]林笠.基于模型诊断算法及应用[D].[博士学位论文],广州:中山大学,2002.
[54]顾煜炯;陈昆亮;杨昆.电站设备故障风险评估方法研究[J].中国电力,2008,41(10):34-39.
[55]张耀辉,郭金茂,徐宗昌.基于故障风险的状态维修检测间隔期的确定[J].中国机械工程,2008,19(5):555-558.
[56] BOWLESJ B.An assessment of RPN prioritization in a failure modes effects andcriticality analysis[J].Proceedings Annual Reliability and Maintainability Sympo-sium,2003:380-385.
[57] PILLAY A,WANG J.Modified failure mode and effects analysis using approximatereasoning[J].Reliability Ensinecfing and System Safety,2003,79:69-85.
[58]戴荣,康锐.RCM决断流程与风险评估[J].航空维修,2001(6):10-11.
[59]王品刚,卓华.基于风险评估的设备维修方法的研究与应用[J].应用技术,2004,2(11):87-88.
[60]董玉亮,顾煜炯,杨昆.基于灰色理论和RCM分析的发电设备风险分析[J].动力工程,2004,24(6):798-800.
[61]耿秀丽,褚学宁.基于故障因果链的故障模式与影响分析风险评估方法[J].计算机集成制造系统,2009,15(12):2473-2480.
[62] Kokawa, M. and S. Shingai. Failure propagating simulation and nonfailure paths se-arch in network systems. Automatica,1982,18(3):335-341.
[63] Kokawa, M, et al. Safety analysis of boil off gas treatment system using failure pro-pagation simulator. In: Annual Conference Records of SICE of Japan,1983,361-362.
[64] M Iri, K Aoki, E O 'shima, H Matsuyama. An Algorithm for Diagnosis of SystemFailures in The Chemical Process Computer&Chemical Engineering,1979,3(l):489-493.
[65] M Iri. et al. A graphical approach to the problem of locating the origin of the systemfailure. Journal of the Operations Res. Soc. of Japan,198O,23(4):295-311.
[66] T Umeda, T Kuriyama. A Graphical Approach to Cause and Effect Analysis of Che-mical Processing Systems.Chemical Engineering Science,1980,35(12):2379-2388.
[67] J.Shiozaki, H. Matsuyama, K. Tano, E. O'shima. Fault Diagnosis of Chemical Proce-sses by the use of Signed, Directed Graphs. Extension to Five-range Patterns of Ab-normality. International Chemical Engineering,1985,25(4):651-659.
[68] Y.Tsuge, J.Shiozaki, H Matsuyama, K.E.O'shima,Y Iguchi, M Fuchigami, M Matsu-shita. Fesibility Study of a Fault-diagnosis System for Chemical Plants InternationalChemical Engineering,1985,25(4):660-667.
[69] M.A.Kramer, B. L. Palowitch. A Rule-Based Approach to Fault Diagnosis Using theSigned Directed Graph. AICHE Journal,1987,33(7):1607-1078.
[70] J.Shiozaki, B.Shibata, H Matsuyama, E O'shima. Fault Diagnosis of Chemical Pro-cesses Utilizing Signed Directed Graphs-Improvement by Using Temporal Informa-tion. IEEE Transactions on Industrial Electronics,1989,36(4):469-474.
[71] C.C.Chang, C.C.Yu. On-Line Fault Diagnosis Using the Signed Directed Graph Ind.Eng. Chem. Res,1990,29(7):1290-1299.
[72] Oyeleye, O. O. and M. A. Kramer Qualitative simulation of chemical process syste-ms: steady-state analysis AICHE,1998,34(9):1441-1454,1988.
[73] Oyeleye, O. O. Qualitative Modeling of Process Behavior and Applications to FaultDiagnosis: PhD Thesis Cambridge: MIT,1988.
[74] Oyeleye, O. O, F.E.Finch and M. A. Kramer. Qualitative Modeling and Fault Diag-nosis of Dynamic Processes by MIDAS. Chem. Eng. Comm.,1990,96:205-228.
[75] Wilcox N. A. and D. M. Himmelblau. The possible cause and effect graphs (PCEG)model for fault diagnosis-1. Methodology. Comput.&Chem. Engng,1994,18(2):103-116.
[76] Wilcox N.A. and D.M. Himmelblau. The possible cause and effect graphs (PCEG)model for fault diagnosis-11. Applications. Comput.&Chem. Engng,1994,18(2):117-127.
[77] Wilcox N.A. The possible cause-effects graph model of process fault diagnosis: Ph.D. Thesis. Austin: The University of Texas,1992.
[78] C.C.Yu, C.Lee. Fault Diagnosis Based on Qualitative/Quantitative Process Know-ledge. AIChE Journal,1991,37(4):617-625.
[79] X.Z.Wang, S.A.Yang, S.H.Yang, C.Mcgreavy. The Application of Fuzzy Qualitativesimulation in Safety and Operability Assessment of Process Plants. In: Rhodes, Gr-eece: Elsevier, Computers Chem. Engng,1996:671-676.
[80] E.E.Tarifa, N.J.Scenn. Fault Diagnosis, Direct Graphs, and Fuzzy Logic ComputersChem. Engng,1997,21(Suppl.):649-654.
[81] E.E.Tarifa and N.J.Scenna. Methodology for fault diagnosis in large chemical pro-cesses and an application to a multistage flash desalination process: PartⅠ. Relia-bility Engineering&System Safety,1998,60(l):29-40.
[82] E.E.Tarifa and N.J.Scenna. Methodology for fault diagnosis in large chemical pro-cesses and an application to a multistage flash desalination process: Part Ⅱ. Re-liability Engineering&System Safety,1998,60(l):41-51.
[83] Kim H. Lee K.Fuzzy implications of fuzzy cognitive map with emphasis on fuzzycausal relationship and fuzzy partially causal relationship.Fuzzy Sets and Systems.1998,97(3):303-313.
[84] Wang X. Yang S.Veloso E.et al.Qualitative Process modeling-a fuzzy signed di re-cted graph mehtod. Computers and Chemical Engineering.1995,19(Suppl.):S735-S740.
[85] Genovesi A. Harmand J.Steyer J P.A fuzzy logic based diagnosis system for the online supervision of anaerobic digestor Pilot-Plant..Biochemical Engineering Joumal.1999,3(3):171-183.
[86] LI R. Wang X..Qualitative/quantitative simulation of process temporal behaviorusing clustered fuzzy digrapahs.American Instiutte of Chemical Engineers Jounral.2001,47(4):906-919
[87] Venkatasubrmanaina,V.ZhaoJ,Viswnahatna S. Intelligent system for HAZOP analy-sis of complex Process Plants. ComPuters and Chemical Engineering,2000,24(9):2291-2302.
[88] Dash S,Venkatasubramanian V.Challenges in the industrial applications of faultdiagnostic systems.ComPuters and Chemical Engineering,2000,24(2):785-791.
[89] Mylaraswmay D.Venkatasubramanian V.A hybrid framework for large scale Processfault diagnosis.ComPuters and Chemical Engineering.1997,21(l):935-940.
[90] Vaidhyanathan R. Venkatasubramanian V.Experience with an expert system of auto-mated HAZOP analysis.Computers and Chemical Engineering.1996,20(l):89-1594.
[91] Vedam H. Venkatasubramanian V. Signed Digraph Based Multiple Fault DiagnosisComputers Chem. Engng,1997,21:655-660.
[92] Wang H. Song ZH. Wang H.Statistical Process monitoring using improved PCA withoptimized sensor locations. Journal of Process Conrtol,2002,12(6):735-744.
[93] Maurya M R. Rengaswamy R. Venkatasubramanian V.A Systematic framework forthe development and analysis of signed digraphs for chemical processes.I. Algorit-hms and analysis. Industrial and Engineering Chemistry Research.2003.42(20):4789-4810
[94] Maurya M R. Rengaswamy R. Venkatasubramanian V.A Systematic framework forthe development and analysis of signed digraphs for chemical processes.II.Controlloops and flowsheet analysis. Industrial and Engineering Chemistry Research.2003.42(2):4811-4827.
[95] Maurya MR, Rengaswamy R, Venkatasubramanian V. A signed directed graph ba-sed systematic framework for steady state malfunction diagnosis inside controlloops [J].Chemical Engineering Science,2006,61(6):1790-1810.
[96] Maurya MR, Rengaswamy R, Venkatasubramanian V. Application of signed digra-phs based analysis for fault diagnosis of chemical process flow sheets[J]. Enginee-ring Applications Artificial Intelligence,2004,17(5):501-518.
[97]杨帆,萧德云.SDG建模及其应用的进展[J].控制理论与应用,2005(5):767-774.
[98]曹文亮.基于符号有向图的热力系统故障诊断方法研究[D].华北电力大学,2006.
[99]李安峰,夏涛,张贝克,等.化工过程SDG建模方法.系统仿真学报,2003,15(10):1364-1368.
[100]刘春雷,吴重光.化工故障诊断自动解释系统的设计与开发.石油化工自动化,2004(l):28-30.
[101] V.Venkatasubramanian, J.Zhao, S.Viswanathan. Intelligent System for HAZOPAnalysis of Complex Process Plants. Computers and Chemical Engineering,2000,24(9):2291-2302.
[102] Zhnang Z Q,Wu C G Zhang B K,et al.SDG multiple fault diagnosis by real-timeinverse inference.Relibaility Engineering and System Safty,2005,87(2):173-189.
[103]沈翠霞.SDG建模与HAZOP故障模式研究,［硕士学位论文］.北京:北京化工大学,2005.
[104]黄卫东,王克昌.基于定性和定量关系的液体火箭发动机故障诊断.航空动力学报,1996,11(3):281-284.
[105]黄卫东,王克昌.基于深层知识规则的液体火箭发动机故障诊断.宇航学报,1997,18(4):61-65.
[106]刘洪刚,吴建军,陈启智.基于定性定量知识集成的液体火箭发动机故障诊断方法.航空动力学报,2002,17(3):377-380.
[107]刘洪刚,吴建军,陈启智.基于定性推理与定量仿真集成的故障诊断.推理系统仿真学报,2003,15(3):689-692.
[108]邓聚龙.灰理论基础.武汉:华中科技大学出版社,2002.
[109]党耀国,刘思峰,刘斌等.聚类系数无显著性差异下的灰色综合聚类方法研究,中国管理科学,2005:Vol.13, No.4:69-73.
[110]韩光臣,孙树栋,王军强,等.复杂系统模糊概率故障图模型研究[J].中国机械工程,2005,16(9):801-804.
[111]于达仁,王伟,胡清华,等.除氧器故障诊断的含未测节点的符号有向图方法[J].汽轮机技术,2008,50(1):39-42.
[112]张东民,廖文和,程筱胜,等.复杂机械产品的层级实例库研究[J].中国机械工程,2009,20(1):48-51.
[113]周虹,左洪福.基于功能结构树的复杂系统SDG自动建模研究[J].中国机械工程,2011,20(1):48-51.
[114]周虹,左洪福,蔡景,苏艳.基于TMSDG的民用飞机故障诊断与隔离策略研究[J].航空学报,2012,(03):48-51.
[115]田仲.测试性分配方法研究[J].北京航空航天大学学报,1999,V25(5):607-610.
[116]张巧炫,龙兵,杨兴霁.基于多信号模型的可测性指标体系研究[J].电子测量技术,2011,34(10):19-22.
[117]刘敏华.基于SDG模型的故障诊断及应用研究[D].[博士学位论文],北京:清华大学,2005.
[118] SHIOZAKI J,MATSUYAMA H,O’SHIMA E,et al. An improved algorithm for diag-nosis of system failures in the chemical process[J]. Computers and Chemical Engin-eering,1985,9(3):285-293.
[119] SHIOZAKI J,MATSUYAMA H,TANO K,et al. Fault diagnosis of chemical proce-sses by the use of signed,directed graphs. Extension to five-range patterns of abnor-mality [J]. International Chemical Engineering,1985,25(4):651-659.
[120]崔文利.民航飞机故障诊断方法研究[D].中国民航大学,2008.
[121]石君友,田仲.故障诊断策略的优化方法[J].航空学报,2003,24(3):212-215.
[122]高磊,吕振中,景小宁.飞机实时测试序列生成算法研究及仿真[J].计算机工程与应用,2006(09):228-230.
[123]王伟,胡清华,于霄等.多值属性系统的故障诊断策略最优化方法[J].仪器仪表学报,2008,29(5):1073-1078.
[124] Fang Tu, Pattipati K R. Rollout strategies for sequential fault diagnosis[J]. Systems,Man and Cybernetics, IEEE Transactions on,2003,33(1).
[125]王红霞,叶晓慧,田树新.基于广义AO*算法的测试序列问题研究[J].兵工学报,2010,31(2):204-208.
[126] Garey M R,Graham RL.Performance bounds on the splitting algorithm for binarytesting[J].Acta Infomaation,1974(3):347-355.
[127] Pattpati K R,Alexandridis M.Application of heuristic search and information theoryto sequential fault diagnosis[J].IEEE Transactions onSystem,Man,and Cybemetics,1990,20(4):872-887.
[128]徐章遂,房立清,王希武,等.故障信息诊断原理及应用[M].北京:国防工业出版社,2000.
[129]康锐,石荣德. FMECA技术及其应用[M].北京:国防工业出版社,2006.
[130]曾声奎、赵廷弟、张建国,等.系统可靠性设计分析教程[M].北京:北京航空航天大学出版社,2001.
[131]余锋杰,柯映林,应征.飞机自动化对接装配系统的故障维修决策[J].计算机集成制造系统,2009,15(9):1823-1830.
[2]吴海桥.现代大型客机故障诊断专家系统的研究与开发[D].[博士论文],南京:南京航空航天大学,2002.
[3]张萍,王桂增,周东华.动态系统的故障诊断方法[J].控制理论与应用,2000,17(2):153-158.
[4]窦金生,汤天浩.基于知识的故障诊断技术及其在船舶上的应用[J].船舶工程,2007,29(4):27,72-74.
[5]周东华,叶银忠.现代故障诊断与容错控制[M].北京:清华大学出版社,2000.
[6]张明川.人工神经网络技术在民用涡扇发动机故障诊断中的应用.航空工程与维修,2000,(2):29-31.
[7]蒋东翔,王风雨,周明,等.模糊自组织神经网络在航空发动机故障诊断中的应用.航空动力学报,2001,16(1):80-82.
[8] Venkat V.Raghunathan R.Kewen Y.et al. A review of process fault detection and diagnosis PartⅠ:quantitative model-based methods. Computers and Chemical Engineering,2003,27(3):293-311.
[9] Venkat V.Raghunathan R.Kewen Y.et al. A review of process fault detection and diagnosis PartⅡ:qualitative models and search strategies. Computers and Chemical Engineering,2003,27(3):313-326.
[10] Venkat V.Raghunathan R.Kewen Y.et al. A review of process fault detection and diagnosis PartⅢ:process history based methods. Computers and Chemical Engineering,2003,27(3):327-346.
[11] Kuiper B.J. Qualitative Reasoning-Modeling&Simulation with Incomplete Knowledge. Cam-bridge: MIT Press,1994.
[12] Frank P M. Analytical and qualitative model-based fault diagnosis-A survey and some newresults [J]. European Journal of Control,1996,2(1):6-28.
[13] Vedam H. Venkatasubramanian V. Signed Digraph Based Multiple Fault Diagnosis ComputersChem. Engng,1997,21:655-660.
[14]杜殿林,张光红,吴重光.基于知识故障诊断系统所用的深层知识及SDG方法[J].化工自动化及仪表,2005,32(4):8-10.
[15]吴重光.基于符号定向图(SDG)深层知识模型的定性仿真[J].系统仿真学报,2003,(10):351-1355.
[16]王文辉,周东华.基于定性和半定性方法的故障检测与诊断技术[J].控制理论与应用,2002,19(5):653-659.
[17]高伟,邢琰,王南华.基于定性模型的故障诊断方法[J].空间控制技术与应用,2009,35(1):25-29.
[18] de Kleer J, Brown J. A qualitative physics based on confluence[J]. Artificial Intelli-gence,1984,24(1/3):7-83.
[19] Kuipers B. Qualitative simulation [J].Artificial Intelligence,1986,29(3):289-388.
[20] Forbus K. Qualitative process theory[J].Artificial Intelligence,1984,24(1/3):85-168.
[21] Iwasaki Y, Simon H A. Causality in device behavior[J].Artificial Intelligence,1991,51(1/3):11-38.
[22]孙天.红外测温技术在发动机故障诊断中的应用[J].工程机械,2006(12):60-61.
[23]黄选红.孔探检测在航空发动机维护中的应用[J].机械,2009,(8):66-69.
[24]杨善水,刘祥,严仰光.飞机电源系统BIT技术的应用及发展[J].南京航空航天大学学报,1998,30(5):593-596.
[25]石山等.飞机机电BIT技术[M].北京:国防工业出版社,2010.
[26]常夏.电子中央监控系统(ECAM系统)计算机仿真:[硕士学位论文].西安:西安工业大学,2009.
[27]顾铮.空中客车公司的数字化维修技术[J].中国民航学院学报,2005,23(4):26-28.
[28]张加圣,王海涛.飞机维修系统的演变[J].航空工程与维修,2007,(2):25-26.
[29]马磊.国外先进的飞行器故障诊断工具[J].质量与可靠性,2009(5):50-54.
[30] SOMNATH Deb,KRISHHA R Pattipati,VIJAY Raghavan,et al. Multi-signal flowgraphs:a novel approach for system testability analysis and fault diagnosis[C]//1994IEEE Autotestcon Proceedings IEEE,Anaheim,1994:361-373.
[31] SOMNATH Deb,KRISHHA R Pattipati,ROSHAN shrestha.QSI’s integrated diagno-setics toolset[C]//1997IEEE Autotestcon Proceedings IEEE,Anaheim,1997:408-421.
[32] Somnath Deb,Sudipto Ghoshal,Amit Mathur,etal. Multi-signal modeling for diagno-sis, FMECA,and reliability[C]//Proceedings of the IEEE International Conferenceon Systems,Man,and Cybernetics. IEEE,San Diego,1998:3026-3031.
[33]刘松风,朱明初,叶晓慧.基于多信号模型的故障诊断算法研究[J].计算机测量与控制,2009,17(6):1042-1044.
[34] Pattipati K R Raghavan V,Shakeri M,et a1.TEAMS:Testability Engineering AndMaintenance System[C].Invited paper,1994:1989-1996.
[35] Mathur,S.Deb and K.R. Pattipati.Modeling and Real-time Diagnosis、with TEAMSRT. American Control Conference,Philadelphia, June1998.
[36] Deb S,Pattipati K R,Shrestha R.QSI’S Integrated Toolset[C].Proceeding of the IEEEAUTOTESTCON,1997:408-421.
[37]孔令宽.基于多信号模型的卫星故障诊断技术[硕士学位论文],长沙:研究国防科学技术大学,2009.
[38] KleerJ, WilliamsB C. Diagnosing multiple faults[J].Artificial Intelligence,1987,32:97-130.
[39] Sandra C.Hayden,AdamJ.Swee,Scott E.Christa. Livingstone Model-Based Diagnosisof Earth Observing One[C]. AIAA1st Intelligent Systems Technical Conference,Chicago, Illi-nois,2004.
[40] Siamak T, Sun X H. Inference techniques for diagnosisbased on set operations[C].AIAA the1st Intelligent Systems Technical Conference, Chicago, USA, Sep20-22,2004.
[41]代树武.航天器自主运行关键技术的研究[D].中国科学院博士后学位论文,2002.
[42] Narasimhan,S.,Dearden,R.,Benazera,E.Combining Particle Filter sandConsistency-Based Approaches for Monitoring and Diagnosis of Stochastic HybridSystems[C].15th Inter-national Workshop on Principles of Diagnosis(DX04), Carca-ssonne,France,2004.
[43] Steven A.Soots,Bradley T.Burchett. Dynamic Fuzzy Models of the Fastrac StartupSequence for Fault Detection[C].46th AIAA Aerospace Sciences Meeting and Ex-hibit,Reno,Neva-da,2008.
[44] Karsai,G.,Sztipanovits,J.,Ledeczi,A.,etal. Model-Integrated Development of Em-bedded Software [J].Proceedings of the IEEE,2003,91(1):145-164.
[45] Bradley T. Burchett. Discrete Time Linear Models of the Fastrac Startup Sequencefor Fault Detection[C].46thAIAA Aerospace Sciences Meeting and Exhibit, Reno,Nevada,2008.
[46]龙兵.多信号建模与故障诊断方法及其在航天器中的应用研究,[D].[博士学位论文],哈尔滨:哈尔滨工业大学,2005.
[47]张鹏,王婷婷,俞利明.改进的模糊Petri网在民航飞机故障诊断中的应用[J].测控技术,2011,30(4):70-73.
[48]王修岩,薛斌斌,李宗帅.基于Petri网的飞机交流发电机故障诊断系统研究[J].中国民航大学学报,2012.
[49]徐新海.基于层次SDG的航空发动机故障诊断方法研究[硕士学位论文].南京:南京航空航天大学,2010.
[50]马存宝,周方旺,周建民,黄爱军.基于有向序图模型的飞机燃油系统故障诊断的系统设计与实现[J].测控技术,2011,30(11):110-113.
[51]王勉宇.基于图论的故障诊断研究[D].[硕士学位论文］,杭州:浙江大学,2002.
[52]代树武.航天器自主运行关键技术的研究[D].中国科学院博士后学位论文,2002.
[53]林笠.基于模型诊断算法及应用[D].[博士学位论文],广州:中山大学,2002.
[54]顾煜炯;陈昆亮;杨昆.电站设备故障风险评估方法研究[J].中国电力,2008,41(10):34-39.
[55]张耀辉,郭金茂,徐宗昌.基于故障风险的状态维修检测间隔期的确定[J].中国机械工程,2008,19(5):555-558.
[56] BOWLESJ B.An assessment of RPN prioritization in a failure modes effects andcriticality analysis[J].Proceedings Annual Reliability and Maintainability Sympo-sium,2003:380-385.
[57] PILLAY A,WANG J.Modified failure mode and effects analysis using approximatereasoning[J].Reliability Ensinecfing and System Safety,2003,79:69-85.
[58]戴荣,康锐.RCM决断流程与风险评估[J].航空维修,2001(6):10-11.
[59]王品刚,卓华.基于风险评估的设备维修方法的研究与应用[J].应用技术,2004,2(11):87-88.
[60]董玉亮,顾煜炯,杨昆.基于灰色理论和RCM分析的发电设备风险分析[J].动力工程,2004,24(6):798-800.
[61]耿秀丽,褚学宁.基于故障因果链的故障模式与影响分析风险评估方法[J].计算机集成制造系统,2009,15(12):2473-2480.
[62] Kokawa, M. and S. Shingai. Failure propagating simulation and nonfailure paths se-arch in network systems. Automatica,1982,18(3):335-341.
[63] Kokawa, M, et al. Safety analysis of boil off gas treatment system using failure pro-pagation simulator. In: Annual Conference Records of SICE of Japan,1983,361-362.
[64] M Iri, K Aoki, E O 'shima, H Matsuyama. An Algorithm for Diagnosis of SystemFailures in The Chemical Process Computer&Chemical Engineering,1979,3(l):489-493.
[65] M Iri. et al. A graphical approach to the problem of locating the origin of the systemfailure. Journal of the Operations Res. Soc. of Japan,198O,23(4):295-311.
[66] T Umeda, T Kuriyama. A Graphical Approach to Cause and Effect Analysis of Che-mical Processing Systems.Chemical Engineering Science,1980,35(12):2379-2388.
[67] J.Shiozaki, H. Matsuyama, K. Tano, E. O'shima. Fault Diagnosis of Chemical Proce-sses by the use of Signed, Directed Graphs. Extension to Five-range Patterns of Ab-normality. International Chemical Engineering,1985,25(4):651-659.
[68] Y.Tsuge, J.Shiozaki, H Matsuyama, K.E.O'shima,Y Iguchi, M Fuchigami, M Matsu-shita. Fesibility Study of a Fault-diagnosis System for Chemical Plants InternationalChemical Engineering,1985,25(4):660-667.
[69] M.A.Kramer, B. L. Palowitch. A Rule-Based Approach to Fault Diagnosis Using theSigned Directed Graph. AICHE Journal,1987,33(7):1607-1078.
[70] J.Shiozaki, B.Shibata, H Matsuyama, E O'shima. Fault Diagnosis of Chemical Pro-cesses Utilizing Signed Directed Graphs-Improvement by Using Temporal Informa-tion. IEEE Transactions on Industrial Electronics,1989,36(4):469-474.
[71] C.C.Chang, C.C.Yu. On-Line Fault Diagnosis Using the Signed Directed Graph Ind.Eng. Chem. Res,1990,29(7):1290-1299.
[72] Oyeleye, O. O. and M. A. Kramer Qualitative simulation of chemical process syste-ms: steady-state analysis AICHE,1998,34(9):1441-1454,1988.
[73] Oyeleye, O. O. Qualitative Modeling of Process Behavior and Applications to FaultDiagnosis: PhD Thesis Cambridge: MIT,1988.
[74] Oyeleye, O. O, F.E.Finch and M. A. Kramer. Qualitative Modeling and Fault Diag-nosis of Dynamic Processes by MIDAS. Chem. Eng. Comm.,1990,96:205-228.
[75] Wilcox N. A. and D. M. Himmelblau. The possible cause and effect graphs (PCEG)model for fault diagnosis-1. Methodology. Comput.&Chem. Engng,1994,18(2):103-116.
[76] Wilcox N.A. and D.M. Himmelblau. The possible cause and effect graphs (PCEG)model for fault diagnosis-11. Applications. Comput.&Chem. Engng,1994,18(2):117-127.
[77] Wilcox N.A. The possible cause-effects graph model of process fault diagnosis: Ph.D. Thesis. Austin: The University of Texas,1992.
[78] C.C.Yu, C.Lee. Fault Diagnosis Based on Qualitative/Quantitative Process Know-ledge. AIChE Journal,1991,37(4):617-625.
[79] X.Z.Wang, S.A.Yang, S.H.Yang, C.Mcgreavy. The Application of Fuzzy Qualitativesimulation in Safety and Operability Assessment of Process Plants. In: Rhodes, Gr-eece: Elsevier, Computers Chem. Engng,1996:671-676.
[80] E.E.Tarifa, N.J.Scenn. Fault Diagnosis, Direct Graphs, and Fuzzy Logic ComputersChem. Engng,1997,21(Suppl.):649-654.
[81] E.E.Tarifa and N.J.Scenna. Methodology for fault diagnosis in large chemical pro-cesses and an application to a multistage flash desalination process: PartⅠ. Relia-bility Engineering&System Safety,1998,60(l):29-40.
[82] E.E.Tarifa and N.J.Scenna. Methodology for fault diagnosis in large chemical pro-cesses and an application to a multistage flash desalination process: Part Ⅱ. Re-liability Engineering&System Safety,1998,60(l):41-51.
[83] Kim H. Lee K.Fuzzy implications of fuzzy cognitive map with emphasis on fuzzycausal relationship and fuzzy partially causal relationship.Fuzzy Sets and Systems.1998,97(3):303-313.
[84] Wang X. Yang S.Veloso E.et al.Qualitative Process modeling-a fuzzy signed di re-cted graph mehtod. Computers and Chemical Engineering.1995,19(Suppl.):S735-S740.
[85] Genovesi A. Harmand J.Steyer J P.A fuzzy logic based diagnosis system for the online supervision of anaerobic digestor Pilot-Plant..Biochemical Engineering Joumal.1999,3(3):171-183.
[86] LI R. Wang X..Qualitative/quantitative simulation of process temporal behaviorusing clustered fuzzy digrapahs.American Instiutte of Chemical Engineers Jounral.2001,47(4):906-919
[87] Venkatasubrmanaina,V.ZhaoJ,Viswnahatna S. Intelligent system for HAZOP analy-sis of complex Process Plants. ComPuters and Chemical Engineering,2000,24(9):2291-2302.
[88] Dash S,Venkatasubramanian V.Challenges in the industrial applications of faultdiagnostic systems.ComPuters and Chemical Engineering,2000,24(2):785-791.
[89] Mylaraswmay D.Venkatasubramanian V.A hybrid framework for large scale Processfault diagnosis.ComPuters and Chemical Engineering.1997,21(l):935-940.
[90] Vaidhyanathan R. Venkatasubramanian V.Experience with an expert system of auto-mated HAZOP analysis.Computers and Chemical Engineering.1996,20(l):89-1594.
[91] Vedam H. Venkatasubramanian V. Signed Digraph Based Multiple Fault DiagnosisComputers Chem. Engng,1997,21:655-660.
[92] Wang H. Song ZH. Wang H.Statistical Process monitoring using improved PCA withoptimized sensor locations. Journal of Process Conrtol,2002,12(6):735-744.
[93] Maurya M R. Rengaswamy R. Venkatasubramanian V.A Systematic framework forthe development and analysis of signed digraphs for chemical processes.I. Algorit-hms and analysis. Industrial and Engineering Chemistry Research.2003.42(20):4789-4810
[94] Maurya M R. Rengaswamy R. Venkatasubramanian V.A Systematic framework forthe development and analysis of signed digraphs for chemical processes.II.Controlloops and flowsheet analysis. Industrial and Engineering Chemistry Research.2003.42(2):4811-4827.
[95] Maurya MR, Rengaswamy R, Venkatasubramanian V. A signed directed graph ba-sed systematic framework for steady state malfunction diagnosis inside controlloops [J].Chemical Engineering Science,2006,61(6):1790-1810.
[96] Maurya MR, Rengaswamy R, Venkatasubramanian V. Application of signed digra-phs based analysis for fault diagnosis of chemical process flow sheets[J]. Enginee-ring Applications Artificial Intelligence,2004,17(5):501-518.
[97]杨帆,萧德云.SDG建模及其应用的进展[J].控制理论与应用,2005(5):767-774.
[98]曹文亮.基于符号有向图的热力系统故障诊断方法研究[D].华北电力大学,2006.
[99]李安峰,夏涛,张贝克,等.化工过程SDG建模方法.系统仿真学报,2003,15(10):1364-1368.
[100]刘春雷,吴重光.化工故障诊断自动解释系统的设计与开发.石油化工自动化,2004(l):28-30.
[101] V.Venkatasubramanian, J.Zhao, S.Viswanathan. Intelligent System for HAZOPAnalysis of Complex Process Plants. Computers and Chemical Engineering,2000,24(9):2291-2302.
[102] Zhnang Z Q,Wu C G Zhang B K,et al.SDG multiple fault diagnosis by real-timeinverse inference.Relibaility Engineering and System Safty,2005,87(2):173-189.
[103]沈翠霞.SDG建模与HAZOP故障模式研究,［硕士学位论文］.北京:北京化工大学,2005.
[104]黄卫东,王克昌.基于定性和定量关系的液体火箭发动机故障诊断.航空动力学报,1996,11(3):281-284.
[105]黄卫东,王克昌.基于深层知识规则的液体火箭发动机故障诊断.宇航学报,1997,18(4):61-65.
[106]刘洪刚,吴建军,陈启智.基于定性定量知识集成的液体火箭发动机故障诊断方法.航空动力学报,2002,17(3):377-380.
[107]刘洪刚,吴建军,陈启智.基于定性推理与定量仿真集成的故障诊断.推理系统仿真学报,2003,15(3):689-692.
[108]邓聚龙.灰理论基础.武汉:华中科技大学出版社,2002.
[109]党耀国,刘思峰,刘斌等.聚类系数无显著性差异下的灰色综合聚类方法研究,中国管理科学,2005:Vol.13, No.4:69-73.
[110]韩光臣,孙树栋,王军强,等.复杂系统模糊概率故障图模型研究[J].中国机械工程,2005,16(9):801-804.
[111]于达仁,王伟,胡清华,等.除氧器故障诊断的含未测节点的符号有向图方法[J].汽轮机技术,2008,50(1):39-42.
[112]张东民,廖文和,程筱胜,等.复杂机械产品的层级实例库研究[J].中国机械工程,2009,20(1):48-51.
[113]周虹,左洪福.基于功能结构树的复杂系统SDG自动建模研究[J].中国机械工程,2011,20(1):48-51.
[114]周虹,左洪福,蔡景,苏艳.基于TMSDG的民用飞机故障诊断与隔离策略研究[J].航空学报,2012,(03):48-51.
[115]田仲.测试性分配方法研究[J].北京航空航天大学学报,1999,V25(5):607-610.
[116]张巧炫,龙兵,杨兴霁.基于多信号模型的可测性指标体系研究[J].电子测量技术,2011,34(10):19-22.
[117]刘敏华.基于SDG模型的故障诊断及应用研究[D].[博士学位论文],北京:清华大学,2005.
[118] SHIOZAKI J,MATSUYAMA H,O’SHIMA E,et al. An improved algorithm for diag-nosis of system failures in the chemical process[J]. Computers and Chemical Engin-eering,1985,9(3):285-293.
[119] SHIOZAKI J,MATSUYAMA H,TANO K,et al. Fault diagnosis of chemical proce-sses by the use of signed,directed graphs. Extension to five-range patterns of abnor-mality [J]. International Chemical Engineering,1985,25(4):651-659.
[120]崔文利.民航飞机故障诊断方法研究[D].中国民航大学,2008.
[121]石君友,田仲.故障诊断策略的优化方法[J].航空学报,2003,24(3):212-215.
[122]高磊,吕振中,景小宁.飞机实时测试序列生成算法研究及仿真[J].计算机工程与应用,2006(09):228-230.
[123]王伟,胡清华,于霄等.多值属性系统的故障诊断策略最优化方法[J].仪器仪表学报,2008,29(5):1073-1078.
[124] Fang Tu, Pattipati K R. Rollout strategies for sequential fault diagnosis[J]. Systems,Man and Cybernetics, IEEE Transactions on,2003,33(1).
[125]王红霞,叶晓慧,田树新.基于广义AO*算法的测试序列问题研究[J].兵工学报,2010,31(2):204-208.
[126] Garey M R,Graham RL.Performance bounds on the splitting algorithm for binarytesting[J].Acta Infomaation,1974(3):347-355.
[127] Pattpati K R,Alexandridis M.Application of heuristic search and information theoryto sequential fault diagnosis[J].IEEE Transactions onSystem,Man,and Cybemetics,1990,20(4):872-887.
[128]徐章遂,房立清,王希武,等.故障信息诊断原理及应用[M].北京:国防工业出版社,2000.
[129]康锐,石荣德. FMECA技术及其应用[M].北京:国防工业出版社,2006.
[130]曾声奎、赵廷弟、张建国,等.系统可靠性设计分析教程[M].北京:北京航空航天大学出版社,2001.
[131]余锋杰,柯映林,应征.飞机自动化对接装配系统的故障维修决策[J].计算机集成制造系统,2009,15(9):1823-1830.