基于模型的重型燃气轮机气路故障诊断研究
|
摘要
燃气轮机气路故障不仅造成燃机性能下降，影响燃机的经济性，气路故障如果不能被及时发现和维修，还会影响燃气轮机机组运行的安全性和可靠性。气路故障诊断对燃机部件气路通流部分的运行状态进行实时的监测和评价，针对燃气轮机的运行状态合理安排燃机的运行和维修计划，减少不必要维修，最大限度的提高燃气轮机系统的安全性、可靠性和经济性，因此燃气轮机气路故障诊断研究具有重要的理论意义和工程应用价值。本文重点研究了基于模型的重型燃气轮机气路故障诊断方法，基于此建立了基于模型的重型燃气轮机气路故障诊断系统。
首先，建立面向对象的重型燃气轮机自适应气路故障诊断模型。将模型仿真结果与燃气轮机运行实测数据对比验证所建立模型的准确性。建立重型燃气轮机状态空间模型、线性和非线性自适应气路故障诊断模型以及基于扩展卡尔曼滤波器的自适应气路故障诊断模型，用于重型燃气轮机气路故障仿真和诊断。
其次，研究基于模型的重型燃气轮机气路故障诊断精度评价和测量参数优化选择方法。提出一种新的状态能观度分析方法，高能观度状态和高总体能观度测量组具有高故障诊断精度。利用能观度方法分析测量噪声、冗余测量、燃气轮机非线性对燃气轮机各状态能观度和故障诊断精度的影响；分别对基于动态模型和稳态模型的重型燃气轮机气路故障诊断方法进行测量参数优化选择。
再次，研究基于自适应扩展卡尔曼滤波器的重型燃气轮机气路故障过程跟踪。针对扩展卡尔曼滤波器算法的不足，提出基于强跟踪滤波器的燃气轮机自适应气路故障诊断方法，实现对燃气轮机渐变故障和快变故障的准确跟踪。基于卡尔曼滤波器原理对强跟踪滤波器算法进行改进，提高算法对故障幅值、滤波器初值以及测量噪声的鲁棒性。
最后，研究基于模型的燃气轮机欠定气路故障诊断问题，提出基于稀疏贝叶斯学习算法的欠定气路故障诊断方法，用于燃气轮机快变气路故障和传感器故障同时诊断。通过对大量故障诊断实例分析，验证方法的有效性、准确性和较已有方法的优越性。
The unscheduled maintenance or outages of gas turbines due to degradation cannot only induce great additional costs and lost revenues but also influence the safety andreliability of gas turbines. Gas path diagnostics and prognosis can be used to assessengine conditions in real-time and predict the failure time in the future, so the predictivemaintenance actions can be performed. The benefits of predictive maintenance actionsare improved usability, safety and reliability as well as reduced life cycle costs.Therefore, the study of gas path diagnostics has great theoretical significance andpractical value. This dissertation focuses on the research on the model-based gas pathdiagnostic method, and develops model-based gas path diagnostic system of heavy-dutygas turbine.
Firstly, an adaptive generic object-oriented model for gas path diagnostics ofheavy-duty gas turbine is established. The model simulation results match with the fielddata, which confirms the accuracy of the model. The state space model, linear andnonlinear adaptive gas path diagnostic model, and Extended Kalman Filter(EKF) basedadaptive gas path diagnostic model of heavy-duty gas turbine are developed to analyzeand diagnose the gas path fault.
Secondly, the assessment of gas path diagnostic accuracy and optimalmeasurement selections method for model-based gas path diagnostics is studied. Anovel degree of observability analysis method for measurement selections of gas pathdiagnostics is developed. The states with high degree of observability and themeasurement sets with high overall degree of observability result in high estimationaccuracy in gas path diagnostics. Using the proposed method, the influence of themeasurement noise, the overdetermined measurement, the gas turbine nonlinearity andthe advanced measurement such as turbine inlet temperature on degree of observabilityand gas path diagnostic accuracy are analyzed. The optimal measurement selections areconducted for dynamic and stable model-based gas path diagnostics.
Thirdly, the tracking of gas path fault based on adaptive EKF is studied. Accordingto the drawbacks of the EKF, adaptive gas path diagnostics using the strong trackingfilter(STF) is proposed. The proposed method can track both the abrupt fault andgradual fault accurately, which overcomes the drawbacks of the EKF. Based on the analysis of the principle of the EKF, the algorithm of the STF is improved to increasethe robustness against the gas path fault magnitude, initialization value of filter andmeasurement noise.
Finally, the underdetermined gas path diagnostic problem of model-based gas pathdiagnostic method is studied. A new gas path diagnostic method based on SparseBayesian Learning favoring sparse solutions for abrupt fault events is proposed, whichcan be used to diagnose the component fault and sensor bias fault together for abruptfault events. The analysis of the a variety gas path diagnostics results demonstrate thecapability, accuracy and superior over other methods.
首先，建立面向对象的重型燃气轮机自适应气路故障诊断模型。将模型仿真结果与燃气轮机运行实测数据对比验证所建立模型的准确性。建立重型燃气轮机状态空间模型、线性和非线性自适应气路故障诊断模型以及基于扩展卡尔曼滤波器的自适应气路故障诊断模型，用于重型燃气轮机气路故障仿真和诊断。
其次，研究基于模型的重型燃气轮机气路故障诊断精度评价和测量参数优化选择方法。提出一种新的状态能观度分析方法，高能观度状态和高总体能观度测量组具有高故障诊断精度。利用能观度方法分析测量噪声、冗余测量、燃气轮机非线性对燃气轮机各状态能观度和故障诊断精度的影响；分别对基于动态模型和稳态模型的重型燃气轮机气路故障诊断方法进行测量参数优化选择。
再次，研究基于自适应扩展卡尔曼滤波器的重型燃气轮机气路故障过程跟踪。针对扩展卡尔曼滤波器算法的不足，提出基于强跟踪滤波器的燃气轮机自适应气路故障诊断方法，实现对燃气轮机渐变故障和快变故障的准确跟踪。基于卡尔曼滤波器原理对强跟踪滤波器算法进行改进，提高算法对故障幅值、滤波器初值以及测量噪声的鲁棒性。
最后，研究基于模型的燃气轮机欠定气路故障诊断问题，提出基于稀疏贝叶斯学习算法的欠定气路故障诊断方法，用于燃气轮机快变气路故障和传感器故障同时诊断。通过对大量故障诊断实例分析，验证方法的有效性、准确性和较已有方法的优越性。
The unscheduled maintenance or outages of gas turbines due to degradation cannot only induce great additional costs and lost revenues but also influence the safety andreliability of gas turbines. Gas path diagnostics and prognosis can be used to assessengine conditions in real-time and predict the failure time in the future, so the predictivemaintenance actions can be performed. The benefits of predictive maintenance actionsare improved usability, safety and reliability as well as reduced life cycle costs.Therefore, the study of gas path diagnostics has great theoretical significance andpractical value. This dissertation focuses on the research on the model-based gas pathdiagnostic method, and develops model-based gas path diagnostic system of heavy-dutygas turbine.
Firstly, an adaptive generic object-oriented model for gas path diagnostics ofheavy-duty gas turbine is established. The model simulation results match with the fielddata, which confirms the accuracy of the model. The state space model, linear andnonlinear adaptive gas path diagnostic model, and Extended Kalman Filter(EKF) basedadaptive gas path diagnostic model of heavy-duty gas turbine are developed to analyzeand diagnose the gas path fault.
Secondly, the assessment of gas path diagnostic accuracy and optimalmeasurement selections method for model-based gas path diagnostics is studied. Anovel degree of observability analysis method for measurement selections of gas pathdiagnostics is developed. The states with high degree of observability and themeasurement sets with high overall degree of observability result in high estimationaccuracy in gas path diagnostics. Using the proposed method, the influence of themeasurement noise, the overdetermined measurement, the gas turbine nonlinearity andthe advanced measurement such as turbine inlet temperature on degree of observabilityand gas path diagnostic accuracy are analyzed. The optimal measurement selections areconducted for dynamic and stable model-based gas path diagnostics.
Thirdly, the tracking of gas path fault based on adaptive EKF is studied. Accordingto the drawbacks of the EKF, adaptive gas path diagnostics using the strong trackingfilter(STF) is proposed. The proposed method can track both the abrupt fault andgradual fault accurately, which overcomes the drawbacks of the EKF. Based on the analysis of the principle of the EKF, the algorithm of the STF is improved to increasethe robustness against the gas path fault magnitude, initialization value of filter andmeasurement noise.
Finally, the underdetermined gas path diagnostic problem of model-based gas pathdiagnostic method is studied. A new gas path diagnostic method based on SparseBayesian Learning favoring sparse solutions for abrupt fault events is proposed, whichcan be used to diagnose the component fault and sensor bias fault together for abruptfault events. The analysis of the a variety gas path diagnostics results demonstrate thecapability, accuracy and superior over other methods.
引文
[1] Langston, S. New Horizons. ASME Power and Energy Magazine,2005,2(2).
[2] AirBus S.A.S. Global Market Forest2004-2023.
[3]蒋洪德.加速推进重型燃气轮机核心技术研究开发和国产化.动力工程学报,2011,31(8):563-566.
[4]糜洪元.国内外燃气轮机发电技术的发展现状与展望.电力设备.2006,7(10):8-10.
[5] Diakunchak I S. Performance Deterioration in Industrial Gas Turbines. ASME Journalof Engineering for Gas Turbines and Power,1992,114(2):161-168.
[6] Kurz R, Brun K. Degradation in Gas Turbine Systems. ASME Journal of Engineeringfor Gas Turbines and Power,2001,123(1):70-77.
[7] Ogaji S O T, Sampath S, Singh R, Probert S D. Parameter selection for diagnosing agas-turbine's performance-deterioration. Applied Energy.2002,73(1):25-46.
[8] Jaw L C. Recent Advancements in Aircraft Engine Health Management (EHM)Technologies and Recommendations for the Next Step. ASME2005, GT-2005-68625.
[9] Litt J S,Simon D L,Gary S,et al,A Survey of Intelligent Control and HealthManagement Technologies for Aircraft Propulsion Systems,NASA/TM-2005-213622.
[10] Garg S,Controls and Health Management Technologies for Intelligent AerospacePropulsion Systems,AIAA-2004-0949.
[11] Garg S,Propulsion Control and Health Management Research at NASA GlennResearch Center,NASA/TM-2002-211590.
[12]张鹏.基于卡尔曼滤波的航空发动机故障诊断技术研究.南京.南京航空航天大学,2008.
[13] Ed Hindle,Robert Van Stone,Chris Brogan,A Prognostic and Diagnostic Approach toEngine Health Management,ASME2006, GT-2006-90614.
[14]叶志锋.基于模型和神经网络的发动机故障诊断.南京.南京航空航天大学，2003.
[15] Escher P C. Pythia: An Object-Oriented Gas Paht Analysis Computer Program forGeneral Applications. United Kingdom: Cranfield University,1995.
[16] Gülen S C, Griffin P R, Paolucci S. Real-Time On-Line Performace Diagnostics ofHeavy-Duty Industrial Gas Turbines. ASME Journal of Engineering for Gas Turbinesand Power,2002,124(4):910-921.
[17] Sekhon R. Real time prognostic strategies: Application to gas turbines. South Carolina:Clemson University,2007.
[18] Sreedhar R. Fault diagnosis and control of a thermal power plant. Austin: Universityof Texas,1995.
[19] Boeing Commerical Airplanes,"Statistical Summary of Commercial Jet AirplaneAccidents: Worldwide Operations1959-2005."http://www.boeing.com/news/techissues, May2006.[Online; accessed07-March-2007].
[20] Shin J. The NASA Aviation Safety Program-Overview,ASME2000, GT-2000-
[21] Simon D L. An Overview of the NASA Aviation Safety Program Propulsion HealthMonitoring Element. AIAA Paper, AIAA-2000-3624.
[22] Simon D L,Garg S,Venti M. Propulsion Control and Health Management Technologyfor Flight Test on the C-17T-1Aircraft,NASA/TM-2004-213303.
[23] Koop W. The Integrated High Performance Turbine Engine Technology Program.ISABE97-7175,1997.
[24] Friend R. Turbine engine research in the United States Air Force. Proceedings of IEEEAerospace Conference.2001,3165-3178.
[25] Zuniga F A, Maclise D C, Romano D J. Integrated System Health Management forExploration Systems. AIAA Paper, AIAA-2005-2586.
[26] Kumano S, Mikami N, Aoyama K. Advanced Gas Turbine Diagnostics Using PatternRecognition. ASME2011, GT-2011-45670.
[27] Della Villa S A, Koeneke C. A Historical and Current Perspective of the Availabilityand Reliability Performance of Heavy Duty Gas Turbines: Benchmarks andExpectations. ASME2010, GT-2010-23182.
[28] Takahashi K, Akagi K, Nishimura S, etc. Factors for Improving Reliability in LargeIndustrial Gas Turbines. ASME2004, GT-2004-54196.
[29] Engelbert C, Nilsson M, Ariane Sutor D, etc. Application of a Reliability Model toGas Turbine Design. ASME2008, POWER-2008-60100.
[30] Slottner P, W rja M. Knowledge based prognostics models for gas turbine corecomponents. ASME2008, GT-2008-51276.
[31] Bassily H, Daqaq M F, Wagner J. Application of Pseudo-Poincare Maps to Assess GasTurbine System Health. ASME Journal of Engineering for Gas Turbines and Power,2012,134(5),051601.
[32] Bohlin M, W rja M, Holst A. Optimization of condition-based maintenance forindustrial gas turbines: Requirements and results. ASME2009, GT-2009-59935.
[33]陈大光.燃气涡轮发动机的状态监控与故障诊断.航空学报,1989,10(6):B225-B236.
[34]唐耿林,陈大光.子方阵分析法对发动机进行故障诊断.航空动力学报,1990,5(3):213-218.
[35]陈大光,韩凤学,唐耿林.多状态气路分析法诊断发动机故障的分析.航空动力学报,1994,9(4):349-352.
[36]范作民,孙春林,林兆福.发动机故障诊断的主特征量模型.航空学报,1990,11(1):A37-A45.
[37]范作民,孙春林,白杰.航空发动机故障诊断导论.北京,科学出版社,2004.
[38]鲁峰.航空发动机故障诊断的融合技术研究.南京,南京航空航天大学,2009.
[39]黄伟斌.发动机健康管理的自适应机载实时模型.南京,南京航空航天大学,2007.
[40]左洪福.发动机磨损状态监控和故障诊断技术.北京,航空工业出版社,1996.
[41] Xue W, Guo Y Q. Aircraft Engine Sensor Fault Diagnostics Based on Estimation ofEngine's Health Degradation. Chinese Journal of Aeronautics,2009,22(1):18-21.
[42]徐启华,师军.应用SVM的发动机故障诊断若干问题研究.航空学报,26(6):686-690.
[43]肖翔.涡扇发动机传感器故障诊断与容错控制研究.西安,西北工业大学,2004.
[44]翁史烈.燃气轮机性能分析.上海,上海交通大学出版社,1987.
[45]夏迪,王永泓. PG9171E型燃气轮机变工况计算模型的建立.热能动力工程,2008,23(4):338-343.
[46]夏迪.王永泓.基于热参数的单轴燃气轮机非线性故障诊断方法.上海交通大学学报,2008,42(3):381-384.
[47]夏迪.王永泓.燃气轮机非线性故障诊断中梯度计算的新方法.中国电机工程学报,2008,28(14):108-111.
[48] Xia D, Wang Y H, Weng S L. A new method to evaluate the influence coefficientmatrix for gas path analysis. Journal of Mechanical Science and Technology,2009,23(3):667-676.
[49]郝英,孙建国,白杰.航空燃气涡轮发动机气路故障诊断现状与展望.航空动力学报,2003,18(6):753-760.
[50] Zaita A V, Buley G, Karlsons G. Performance Deterioration Modeling in Aircraft GasTurbine Engines. ASME Journal of Engineering for Gas Turbines and Power,1998,120(2):344-349.
[51] Lakshminarasimaha A N, Boyce M P, Meher-Homji C B. Modeling and Analysis ofGas Turbine Performance Deterioration. ASME Journal of Engineering for GasTurbines and Power,1994,116(1):46-52.
[52] Aker G F, Saravanamuttoo H I H, Predicting Gas Turbine Performance DegradationDue to Compressor Fouling Using Computer Simulation Techniques. ASME Journalof Engineering for Gas Turbines and Power,1989,111(2):343-350.
[53] Macleod J D. Computer modelling of implanted component faults in a gas turbineengine. AIAA Paper, AIAA-91-1912.
[54] Batcho P F, Moller J C, Padova C, Dunn M G. Interpretation of gas turbine responsedue to dust ingestion. ASME Journal of Engineering for Gas Turbines and Power,1987,109(2):344-352.
[55] Flashberg, Larry S. Measurement of combustion turbine non-recoverable degradation.5th International Conference and Exhibition for the Power Generating Industries-POWER-GEN '92, Orlando., pp.331-346(1992).
[56] Zhu P, Saravanamutto H I H. Simulation of an advanced twin-spool industrial gasturbine. ASME Journal of Engineering for Gas Turbines and Power,1992,114(4):180-186.
[57] Zwebek A, Pilidis P. Degradation Effects on Combined Cycle Power PlantPerformance-Part I: Gas Turbine Cycle Component Degradation Effects. ASMEJournal of Engineering for Gas Turbines and Power,2003,125(3):651-657.
[58] Zwebek A, Pilidis P. Degradation Effects on Combined Cycle Power PlantPerformance-Part III: Gas and Steam Turbine Component Degradation Effects.ASME Journal of Engineering for Gas Turbines and Power,2004,126(1):306-315.
[59] Urban L A. Parameter selection for multiple fault diagnosis of gas turbine engines.ASME1974,74-GT-6.
[60] Urban L A. Gas Path Analysis Applied to Turbine Engine Condition Monitoring.AIAA Paper, AIAA-1972-1082.
[61] Urban L A, Volponi A J. Mathematical methods of relative engine performancediagnostics. SAE1992Transactions Journal of Aerospace,1992,1(101), Paper No.922048.
[62] Doel D L. TEMPER—A Gas-Path Analysis Tool for Commercial Jet Engines. ASMEJournal of Engineering for Gas Turbines and Power,1994,116(1):82-89.
[63] Doel D L. An Assessment of Weighted-Least-Squares-Based Gas Path Analysis.ASME Journal of Engineering for Gas Turbines and Power,1994,116(2):366-373.
[64] Doel D L. Interpretation of Weighted-Least-Squares Gas Path Analysis Results.ASME Journal of Engineering for Gas Turbines and Power,2003,125(3):624-633.
[65] Stamatis A. Mathioudakis K. Papailiou K D. Adaptive Simulation of Gas TurbinePerformance. ASME Journal of Engineering for Gas Turbines and Power,1990,112(2):168-175.
[66] Aretakis N. Mathioudakis K. Stamatic A. Nonlinear Engine Component FaultDiagnosis From a Limited Number of Measurements Using a Combinatorial Approach.ASME Journal of Engineering for Gas Turbines and Power,2003,125(2):642-650.
[67] Li Y G. Gas Turbine Performance and Health Status Estimation Using Adaptive GasPath Analysis. ASME Journal of Engineering for Gas Turbines and Power,2010,125(4),041701.
[68] Simon D L. Gary S. Optimal Tuner Selection for Kalman Filter-Based Aircraft EnginePerformance Estimation. ASME Journal of Engineering for Gas Turbines and Power,2010,132(3),031601.
[69] Sugiyama N. System Identification of Jet Engines. ASME Journal of Engineering forGas Turbines and Power,2000,122(1):19-26.
[70] Simon D, Simon D L. Aircraft Turbofan Engine Health Estimation Using ConstrainedKalman Filtering. ASME Journal of Engineering for Gas Turbines and Power,2005,127(2):323-328.
[71] Litt J S. An Optimal Orthogonal Decomposition Method for Kalman Filter-BasedTurbofan Engine Thrust Estimation. ASME Journal of Engineering for Gas Turbinesand Power,2008,130(1),011601.
[72] Borguet S, Léonard O. A Generalized Likelihood Ratio Test for Adaptive Gas TurbinePerformance Monitoring. ASME Journal of Engineering for Gas Turbines and Power,2009,131(1),011601.
[73] Lee Y K, Mavris D N, Volovoi V V, etc. A fault diagnosis method for industrial gasturbines using Bayesian data analysis. ASME Journal of Engineering for Gas Turbinesand Power,2010,132(4),041602.
[74] Romessis C, Mathioudakis K. Bayesian Network Approach for Gas Path FaultDiagnosis. ASME Journal of Engineering for Gas Turbines and Power,2006,128(1):64-72.
[75] Kestner B K, Lee Y K, Voleti Gautham, etc. Diagnostics of Highly DegradedIndustrial Gas Turbines Using Bayesian Networks. ASME2011, GT-2011-45249.
[76] Sampath S, Singh R. An Integrated Fault Diagnostics Model Using Genetic Algorithmand Neural Networks. ASME Journal of Engineering for Gas Turbines and Power,2006,128(1):49-56.
[77] Li Y G. An Genetic Algorithm Approach to Estimate Performance Status of GasTurbines. ASME2008, GT-2008-50175.
[78] Antonisse J. A new interpretation of schema notation that overturns the binaryencoding constraint. Proceedings of the Third InternationalConference on GeneticAlgorithms,1989.
[79] Bettocchi R, Pinelli M, Spina P R, etc. Artificial Intelligence for the Diagnostics ofGas Turbines-Part I: Neural Network Approach. ASME Journal of Engineering forGas Turbines and Power,2007,129(3):711-719.
[80] Bettocchi R, Pinelli M, Spina P R, etc. Artificial Intelligence for the Diagnostics ofGas Turbines-Part I I: Neuro-Fuzzy Approach. ASME Journal of Engineering for GasTurbines and Power,2007,129(3):720-729.
[81] Lu P J, Zhang M C, Hsu T C, Zhang J. An Evaluation of Engine Faults DiagnosticsUsing Artificial Neural Networks. ASME Journal of Engineering for Gas Turbines andPower,2001,123(2):340-346.
[82] Romesis C, Mathioudakis K. Setting Up of a Probabilistic Neural Network for SensorFault Detection Including Operation With Component Faults. ASME Journal ofEngineering for Gas Turbines and Power,2003,125(3):64-641.
[83] Lee S M, Choi W J, Roh T S, etc. A Study on Separate Learning Algorithm UsingSupport Vector Machine for Defect Diagnostics of Gas Turbine Engine. Journal ofMechanical Science and Technology,2008,22(12):2489-2497.
[84] Lee S M, Roh T S, Choi W J. Defect diagnostics of SUAV gas turbine engine usinghybrid SVM-artificial neural network method. Journal of Mechanical Science andTechnology,2009,23(2):559-568.
[85]尉询楷,陆波,汪诚,等.支持向量机在航空发动机故障诊断中的应用.航空动力学报,2004,19(6):844-848.
[86]徐启华,师军.基于支持向量机的航空发动机故障诊断.航空动力学报,2005,20(2):298-303.
[87] Dunia R, Qin S J, Edgar T F. Identification of faulty sensors using principalcomponent analysis. Process Systems Engineering,1996,42(10):2797-2812.
[88] Gomez G, Lendasse A. Statistical fault isolation with principal component analysis.IFAC safe process2000.
[89] Borguet S, Léonard O. Coupling principal component analysis and Kalman filteringalgorithms for on-line aircraft engine diagnostics. Control Engineering Practice,2009,17(4):494-502.
[90] Ganguli R. Application of Fuzzy Logic for Fault Isolation of Jet Engines. ASMEJournal of Engineering for Gas Turbines and Power,2003,125(3):617-623.
[91] Martis D. Fuzzy Logic Estimation Applied to Newton Methods for Gas Turbines.ASME Journal of Engineering for Gas Turbines and Power,2007,129(1):88-96.
[92] Eustace R W. A Real-World Application of Fuzzy Logic and Influence Coefficientsfor Gas Turine Performance Diagnostics. ASME Journal of Engineering for GasTurbines and Power,2008,130(6),061601.
[93] Doel D L. The Role of Expert Systems in Commercial Gas Turbine Engine Monitoring.ASME1990,90-GT-374.
[94] Torella G. Expert Systems for the Trouble-Shooting and the Diagnostics of Engines.AIAA Paper, AIAA-92-3327.
[95] DePold H R, Gass F D. The Application of Expert Systems and Neural Networks toGas Turbine Prognostics and Diagnostics. ASME Journal of Engineering for GasTurbines and Power,1999,121(4):607-612.
[96] Li Y G. Performance-analysis-based gas turbine diagnostics: A review. Proceedings ofthe Institution of Mechanical Engineers, Part A: Journal of Power and Energy.2002,216:363-377.
[97] Marinai L, Probert D, Singh R. Prospects for aero gas turbine diagnosis: a review.2004,79(1):109-126.
[98] Donat W, Choi K, An W, etc. Data Visualization,Date Reduction and Classifier Fusionfor Intelligent Fault Diagnosis in Gas Turbine Engines. ASME Journal of Engineeringfor Gas Turbines and Power,2008,130(4),041602.
[99] Brotherton T, Volponi A, Luppold R, etc. eSTORM:Enhanced Self-tuning On-boardReal-time Engine Model. IEEE Aerospace Conference,2003,7:3075-3086.
[100] Volponi A J. Foundation of Gas Path Analysis (Part I and II)(von Karman InstituteLecture Series). von Karman Institute,2003, Rhode-Saint-Genèse, Belgium.
[101] Stamatis A, Papailiou K D. Discrete operating condition gas path analysis. EngineCondition Monitoring-Technology and Experience,1988.
[102] Stamatis A, Mathioudakis K, Berios G, etc. Jet engine fault detection with discreteoperating points gas path analysis. Journal of Propulsion and Power.1991,7(6):1043-1048.
[103] Gulati A, Zedda M, Singh R. Gas Turbine Engine and Sensor Multiple Operating PointAnalysis Using Optimization Techniques. AIAA Paper, AIAA-2000-3716.
[104] Escher P C. An objective-oriented gas turbine path analysis computer program forgeneral applications. United Kingdom: Cranfield University,1995.
[105] Kamboukos P, Mathioudakis K. Comparison of Linear and Nonlinear Gas TurbinePerformance Diagnostics. ASME Journal of Engineering for Gas Turbines and Power,2005,127(1):49-56.
[106] Kalman R E. A New Approach to Linear Filtering and Prediction Problems. Journal ofBasic Engineering.1960,82(1):35-45.
[107] Provost, M. COMPASS: a generalized ground-based monitoring system.AGARD-CP-449, Engine Condition Monitoring Technology and Experience,1988.
[108] Luppold R, Roman J, Gallops G, etc. Estimating in-flight engine performancevariations using Kalman filter concepts. AIAA, ASME, SAE, and ASEE, JointPropulsion Conference,25th, Monterey, CA, USA,1989.
[109] Heather H L. A Simulation Study of Turbofan Engine Deterioration Estimation UsingKalman Filtering Techniques. NASA, Technical Memorandum,104233.
[110] Guo T H, Merrill W. A Distributed Fault-Detection and Diagnosis System UsingOnline Parameter Estimation. NASA, Technical Memorandum,104433.
[111] Simon D, Simon D L. Constrained Kalman Filtering Via Density Function Truncationfor Turbofan Engine Health Estimation. NASA/TM-2006-214126.
[112] Simon D, Simon D L. Kalman Filtering with Inequality Constraints for TurbofanEngine Health Estimation. NASA/TM-2003-212111.
[113] Dewallef P, Leonard O. On-line Performance Monitoring and Engine DiagnosticUsing Robust Kalman Filtering Techniques. ASME,2003, GT-2003-38379.
[114] Borguet S, Dewallef P, etc. On-line Transient Engine Diagnostics in a KalmanFiltering Framework. ASME,2005, GT2005-68013.
[115] Espańa M D. On the Estimation Algorithm for Adaptive Performance Optimization ofTurbofan Engines. AIAA Paper, AIAA-93-1823.
[116] Palmer C A. Combining Bayesian belief networks with gas path analysis for test celldiagnostics and overhaul. ASME1998, GT-1998-168.
[117] Volponi A J, Brotherton T, Luppold R. Development of an Information Fusion Systemfor Engine Diagnostics and Health Management. NASA/TM-2004-212924.
[118] Dewallef P, Romessis C, L onard O. Combining Classification Techniques WithKalman Filters for Aircraft Engine Diagnostics. ASME Journal of Engineering for GasTurbines and Power,2006,128(2):281-287.
[119] Loboda I, Yepifanov S, Feldshteyn Y. A Generalized Fault Classification for GasTurbine Diagnostics at Steady States and Transients. ASME Journal of Engineeringfor Gas Turbines and Power,2007,127(4):977-985.
[120] Torella G, Lombardo, G. Neural Networks for the diagnostics of gas turbine engines.ASME,1996,96-TA-39.
[121] Kanelopoulos K, Stamatis A, Mathioudakis K. Incorporating neural networks into gasturbine performance diagnostics. ASME,1997,97-GT-35.
[122] Tang G Yates, C L, Chen D. Comparative study of two neural networks applied to jetengine fault diagnosis. AIAA Paper, AIAA-98-3549.
[123] Volponi A J, Depold H, Ganguli R, etc. The Use of Kalman Filter and Neural Networkmethodologies in Gas Turbine Performance Diagnostics-A Comparative Study. ASMEJournal of Engineering for Gas Turbines and Power,2003,125(4):917-924.
[124]郝英.基于智能技术的民航发动机故障诊断和寿命预测研究.南京,南京航空航天大学,2006.
[125] Roemer M J. Engine Health Monitoring System for Advanced Diagnostic Monitoringfor Gas Turbine Engines. AFRL-PR-WP-TR-1998-2120,1999.
[126] Vapnic V. The Nature of Statistical Learning. New York, Springer,1998.
[127]蒲星星,刘尚明,张文杰,等.一种重型燃气轮机通用模型的建立.热力透平,2012,14(1):7-12.
[128] Bücker, D., Span, R., and Wagner, W. Thermodynamic Property Models for moist Airand Combustion Gases. ASME Journal of Engineering for Gas Turbines and Power,2003,125(1):374-384.
[129] Camporeale, S M., Fortunato, B., and Mastrovito, M. A Modular Code for Real TimeDynamic Simulation of Gas Turbines in Simulink. ASME Journal of Engineering forGas Turbines and Power,2006,128(3):506-517.
[130] Celis, C. Modeling of Variable Inlet Guide Vanes Affects on a One Shaft IndustrialGas Turbine Used in a Combined Cycle Application. ASME,2008, GT2008-50076.
[131]张文杰,刘尚明,蒲星星,等.基于Simulink的单轴重型燃气轮机启动过程仿真研究.燃气轮机技术,2012,25(1):27-32.
[132]蒲星星,刘尚明,周媛,等.重型燃气轮机分段线性化状态空间模型的建立.热力透平,2010,39(4):221-225.
[133] Stamatis, M., Mathioudakis, K., and Papailiou, K. Optimal Measurement and HealthIndex Selection for Gas Turbine Performance Status and Fault Diagnosis. ASMEJournal of Engineering for Gas Turbines and Power,1992,114(2):209-216.
[134] Mathioudakis, K., and Kamboukos, Ph. Assessment of the Effectiveness of Gas PathDiagnostic Schemes. ASME Journal of Engineering for Gas Turbines and Power,2006,128(1):57-63.
[135] McCusker, J. R., and Danai, K. Measurement Selection for Engine Transients byParameter Signatures. ASME Journal of Engineering for Gas Turbines and Power,2010,132(12):121601-1-121601-10.
[136] Mushini, R., and Simon, D. On optimization of sensor selection for aircraft gas turbineengines. Proceeding of18th International Conference on Systems Engineering,2005,9-18.
[137] Sowers, T. S., Kopasakis, G., and Simon, D. L. Application of the Systematic SensorSelection Strategy for Turbofan Engine Diagnostics. ASME,2008, GT2008-50525.
[138] Z. Chen. Local Observability and Its Application to Multiple Measurement Estimation.1991, IEEE Transactions on Industrial Electronics,38(6):491-496.
[139] Provost, M. J. The Use of Optimal Estimation Techniques in the Analysis of GasTurbines. Cranfield University,1994.
[140] Borguet, S., and Leonard, O. The Fisher Information Matrix as a Relevant Tool forSensor Selection in Engine Health Monitoring. International Journal of RotatingMachinery,2008, p.784749.
[141] Yim, J. R. Autonomous Spacecraft Orbit Navigation. Texas A&M University,2002.
[142] Ham, F. M., and Brown, R. G.. Observability, Eigenvalues, and Kalman Filtering.IEEE Transactions on Aerospace and Electronic Systems,1983, AES19(2):269-273.
[143] Hong, S., Chun, H. H., Kwon, S. H., and Lee, M. H. Observability Measures and TheirApplication to GPS/INS. IEEE Transactions on Vehicular Technology,2008,57(1):97-106.
[144]范炜,李勇.基于奇异值分解的航天器自主导航系统能观度分析.航天控制,2009,27(2):50-55.
[145] Pinelli, M., and Spina, P. Gas turbine field performance determination: sources ofuncertainties. ASME Journal of Engineering for Gas Turbines and Power,2002,124(1):155-160.
[146] Mehra, R K. Approaches to adaptive filtering. IEEE Transactions on AutomaticControl,1972,17(5):693-698.
[147] Alspach, D. L. A parallel filtering algorithm for linear systems with unknowntime-varying noise statistics. IEEE Transactions on Automatic Control,1974,19(5),552-556.
[148] Kashyap, R. L. Maximum likelihood identification of stochastic linear systems. IEEETransactions on Automatic Control,1970,15(1):25-34.
[149] Mehra, R K. On the identification of variances and adaptive Kalman filtering. IEEETransactions on Automatic Control,1970,15(2):175-184.
[150] Odelson, B., Rajamani, M., and Rawlings, J. A New Autocovariance Least-SquaresMethod for Estimating Noise Covariances. Automatica,2006,42(2):303-308.
[151] Jazwinski, A. H. Adaptive Filtering. Automatica,1969,5(4):475-485.
[152] Léonard, O., Borguet, S., and Dewallef, P. Adaptive estimation algorithm for aircraftengine performance monitoring. Journal of Propulsion and Power,2008,24(4):763-769.
[153] Willsky, A., and Jones, H. A Generalized Likelihood Ratio Approach to StateEstimation in Linear Systems Subject to Abrupt Changes. Proceedings of the IEEEConference on Decision and Control, Phoenix, AZ, November,1974,846-853.
[154] Chetouani, Y. Application of the Generalized Likelihood Ratio Test for DetectingChanges in a Chemical Reactor. Process Safety and Environmental Protection,2006,84(5):371-377.
[155] Borguet, S., and Léonard, O. Assessment of an Anomaly Detector for Jet EngineHealth Monitoring. International Journal of Rotating Machinery,2011,942576.
[156]周东华,叶银忠.现代故障诊断与容错控制,北京,清华大学出版社,2000.
[157]周东华,孙优贤.控制系统的故障检测与诊断技术,北京,清华大学出版社,1994.
[158] Zhou, D H., and Frank, P M. Strong tracking filtering of nonlinear time-varyingstochastic systems with coloured noise: application to parameter estimation andempirical robustness analysis. International Journal of Control,1996,65(2):295-307.
[159] Lipowsky, H., Staudacher, S., Bauer, M., and Schmidt, K. J. Application of BayesianForecasting to Change Detection and Prognosis of Gas Turbine Performance. ASMEJournal of Engineering for Gas Turbines and Power,2010,132(3),031602.
[160] Donoho, D. L. Compressed sensing. IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[161] Yang, A. Y., Wright, J., Ma, Y., and Sastry, S. S. Feature selection in face recognition:A sparse representation perspective. UC Berkeley Tech Report UCB/EECS-2007-99.
[162] Zhang, B., Karray, F., Li, Q., and Zhang, L. Sparse Representation Classifier formicroaneurysm detection and retinal blood vessel extraction. Information Sciences,2012,200(1):78-90.
[163] Borguet, S., and Léonard, O. A sparse estimation approach to fault isolation. ASMEJournal of Engineering for Gas Turbines and Power,2010,132(2),021601.
[164] Borguet, S., and Léonard, O. Constrained sparse estimation for improved faultisolation. ASME Journal of Engineering for Gas Turbines and Power,2011,133(12),121602.
[165] Fuchs, J. J. On Sparse Representations in Arbitrary Redundant Bases. IEEETransactions on Information Theory,2004,50(6):1341–1344.
[166] Fuchs, J. J. Recovery of Exact Sparse Representations in the Presence of Noise. IEEEInternational Conference on Acoustics, Speech, and Signal Processing,2004,2:533-536.
[167] Ji, S., Xue, Y., and Carin, L. Bayesian compressive sensing. IEEE Transactions onSignal Processing,2008,56(6):2346-2356.
[168] Wipf, D., and Rao, B.-norm minimization for basis selection. In Advances inNeural Information Processing Systems (NIPS17),2005.
[169] Wipf, D., and Rao, B. Comparing the effects of different weight distributions onfinding sparse representations. In Advances in Neural Information Processing Systems(NIPS18),2006.
[170] Tipping, M. E. Sparse Bayesian learning and the relevance vector machine. Journal ofMachine Learning Research,2001,1:211-244.
[171] Wipf, D. P., and Rao, B. D. Sparse Bayesian learning for basis selection. IEEETransactions on Signal Processing,2004,52(8):2153-2164.
[2] AirBus S.A.S. Global Market Forest2004-2023.
[3]蒋洪德.加速推进重型燃气轮机核心技术研究开发和国产化.动力工程学报,2011,31(8):563-566.
[4]糜洪元.国内外燃气轮机发电技术的发展现状与展望.电力设备.2006,7(10):8-10.
[5] Diakunchak I S. Performance Deterioration in Industrial Gas Turbines. ASME Journalof Engineering for Gas Turbines and Power,1992,114(2):161-168.
[6] Kurz R, Brun K. Degradation in Gas Turbine Systems. ASME Journal of Engineeringfor Gas Turbines and Power,2001,123(1):70-77.
[7] Ogaji S O T, Sampath S, Singh R, Probert S D. Parameter selection for diagnosing agas-turbine's performance-deterioration. Applied Energy.2002,73(1):25-46.
[8] Jaw L C. Recent Advancements in Aircraft Engine Health Management (EHM)Technologies and Recommendations for the Next Step. ASME2005, GT-2005-68625.
[9] Litt J S,Simon D L,Gary S,et al,A Survey of Intelligent Control and HealthManagement Technologies for Aircraft Propulsion Systems,NASA/TM-2005-213622.
[10] Garg S,Controls and Health Management Technologies for Intelligent AerospacePropulsion Systems,AIAA-2004-0949.
[11] Garg S,Propulsion Control and Health Management Research at NASA GlennResearch Center,NASA/TM-2002-211590.
[12]张鹏.基于卡尔曼滤波的航空发动机故障诊断技术研究.南京.南京航空航天大学,2008.
[13] Ed Hindle,Robert Van Stone,Chris Brogan,A Prognostic and Diagnostic Approach toEngine Health Management,ASME2006, GT-2006-90614.
[14]叶志锋.基于模型和神经网络的发动机故障诊断.南京.南京航空航天大学，2003.
[15] Escher P C. Pythia: An Object-Oriented Gas Paht Analysis Computer Program forGeneral Applications. United Kingdom: Cranfield University,1995.
[16] Gülen S C, Griffin P R, Paolucci S. Real-Time On-Line Performace Diagnostics ofHeavy-Duty Industrial Gas Turbines. ASME Journal of Engineering for Gas Turbinesand Power,2002,124(4):910-921.
[17] Sekhon R. Real time prognostic strategies: Application to gas turbines. South Carolina:Clemson University,2007.
[18] Sreedhar R. Fault diagnosis and control of a thermal power plant. Austin: Universityof Texas,1995.
[19] Boeing Commerical Airplanes,"Statistical Summary of Commercial Jet AirplaneAccidents: Worldwide Operations1959-2005."http://www.boeing.com/news/techissues, May2006.[Online; accessed07-March-2007].
[20] Shin J. The NASA Aviation Safety Program-Overview,ASME2000, GT-2000-
[21] Simon D L. An Overview of the NASA Aviation Safety Program Propulsion HealthMonitoring Element. AIAA Paper, AIAA-2000-3624.
[22] Simon D L,Garg S,Venti M. Propulsion Control and Health Management Technologyfor Flight Test on the C-17T-1Aircraft,NASA/TM-2004-213303.
[23] Koop W. The Integrated High Performance Turbine Engine Technology Program.ISABE97-7175,1997.
[24] Friend R. Turbine engine research in the United States Air Force. Proceedings of IEEEAerospace Conference.2001,3165-3178.
[25] Zuniga F A, Maclise D C, Romano D J. Integrated System Health Management forExploration Systems. AIAA Paper, AIAA-2005-2586.
[26] Kumano S, Mikami N, Aoyama K. Advanced Gas Turbine Diagnostics Using PatternRecognition. ASME2011, GT-2011-45670.
[27] Della Villa S A, Koeneke C. A Historical and Current Perspective of the Availabilityand Reliability Performance of Heavy Duty Gas Turbines: Benchmarks andExpectations. ASME2010, GT-2010-23182.
[28] Takahashi K, Akagi K, Nishimura S, etc. Factors for Improving Reliability in LargeIndustrial Gas Turbines. ASME2004, GT-2004-54196.
[29] Engelbert C, Nilsson M, Ariane Sutor D, etc. Application of a Reliability Model toGas Turbine Design. ASME2008, POWER-2008-60100.
[30] Slottner P, W rja M. Knowledge based prognostics models for gas turbine corecomponents. ASME2008, GT-2008-51276.
[31] Bassily H, Daqaq M F, Wagner J. Application of Pseudo-Poincare Maps to Assess GasTurbine System Health. ASME Journal of Engineering for Gas Turbines and Power,2012,134(5),051601.
[32] Bohlin M, W rja M, Holst A. Optimization of condition-based maintenance forindustrial gas turbines: Requirements and results. ASME2009, GT-2009-59935.
[33]陈大光.燃气涡轮发动机的状态监控与故障诊断.航空学报,1989,10(6):B225-B236.
[34]唐耿林,陈大光.子方阵分析法对发动机进行故障诊断.航空动力学报,1990,5(3):213-218.
[35]陈大光,韩凤学,唐耿林.多状态气路分析法诊断发动机故障的分析.航空动力学报,1994,9(4):349-352.
[36]范作民,孙春林,林兆福.发动机故障诊断的主特征量模型.航空学报,1990,11(1):A37-A45.
[37]范作民,孙春林,白杰.航空发动机故障诊断导论.北京,科学出版社,2004.
[38]鲁峰.航空发动机故障诊断的融合技术研究.南京,南京航空航天大学,2009.
[39]黄伟斌.发动机健康管理的自适应机载实时模型.南京,南京航空航天大学,2007.
[40]左洪福.发动机磨损状态监控和故障诊断技术.北京,航空工业出版社,1996.
[41] Xue W, Guo Y Q. Aircraft Engine Sensor Fault Diagnostics Based on Estimation ofEngine's Health Degradation. Chinese Journal of Aeronautics,2009,22(1):18-21.
[42]徐启华,师军.应用SVM的发动机故障诊断若干问题研究.航空学报,26(6):686-690.
[43]肖翔.涡扇发动机传感器故障诊断与容错控制研究.西安,西北工业大学,2004.
[44]翁史烈.燃气轮机性能分析.上海,上海交通大学出版社,1987.
[45]夏迪,王永泓. PG9171E型燃气轮机变工况计算模型的建立.热能动力工程,2008,23(4):338-343.
[46]夏迪.王永泓.基于热参数的单轴燃气轮机非线性故障诊断方法.上海交通大学学报,2008,42(3):381-384.
[47]夏迪.王永泓.燃气轮机非线性故障诊断中梯度计算的新方法.中国电机工程学报,2008,28(14):108-111.
[48] Xia D, Wang Y H, Weng S L. A new method to evaluate the influence coefficientmatrix for gas path analysis. Journal of Mechanical Science and Technology,2009,23(3):667-676.
[49]郝英,孙建国,白杰.航空燃气涡轮发动机气路故障诊断现状与展望.航空动力学报,2003,18(6):753-760.
[50] Zaita A V, Buley G, Karlsons G. Performance Deterioration Modeling in Aircraft GasTurbine Engines. ASME Journal of Engineering for Gas Turbines and Power,1998,120(2):344-349.
[51] Lakshminarasimaha A N, Boyce M P, Meher-Homji C B. Modeling and Analysis ofGas Turbine Performance Deterioration. ASME Journal of Engineering for GasTurbines and Power,1994,116(1):46-52.
[52] Aker G F, Saravanamuttoo H I H, Predicting Gas Turbine Performance DegradationDue to Compressor Fouling Using Computer Simulation Techniques. ASME Journalof Engineering for Gas Turbines and Power,1989,111(2):343-350.
[53] Macleod J D. Computer modelling of implanted component faults in a gas turbineengine. AIAA Paper, AIAA-91-1912.
[54] Batcho P F, Moller J C, Padova C, Dunn M G. Interpretation of gas turbine responsedue to dust ingestion. ASME Journal of Engineering for Gas Turbines and Power,1987,109(2):344-352.
[55] Flashberg, Larry S. Measurement of combustion turbine non-recoverable degradation.5th International Conference and Exhibition for the Power Generating Industries-POWER-GEN '92, Orlando., pp.331-346(1992).
[56] Zhu P, Saravanamutto H I H. Simulation of an advanced twin-spool industrial gasturbine. ASME Journal of Engineering for Gas Turbines and Power,1992,114(4):180-186.
[57] Zwebek A, Pilidis P. Degradation Effects on Combined Cycle Power PlantPerformance-Part I: Gas Turbine Cycle Component Degradation Effects. ASMEJournal of Engineering for Gas Turbines and Power,2003,125(3):651-657.
[58] Zwebek A, Pilidis P. Degradation Effects on Combined Cycle Power PlantPerformance-Part III: Gas and Steam Turbine Component Degradation Effects.ASME Journal of Engineering for Gas Turbines and Power,2004,126(1):306-315.
[59] Urban L A. Parameter selection for multiple fault diagnosis of gas turbine engines.ASME1974,74-GT-6.
[60] Urban L A. Gas Path Analysis Applied to Turbine Engine Condition Monitoring.AIAA Paper, AIAA-1972-1082.
[61] Urban L A, Volponi A J. Mathematical methods of relative engine performancediagnostics. SAE1992Transactions Journal of Aerospace,1992,1(101), Paper No.922048.
[62] Doel D L. TEMPER—A Gas-Path Analysis Tool for Commercial Jet Engines. ASMEJournal of Engineering for Gas Turbines and Power,1994,116(1):82-89.
[63] Doel D L. An Assessment of Weighted-Least-Squares-Based Gas Path Analysis.ASME Journal of Engineering for Gas Turbines and Power,1994,116(2):366-373.
[64] Doel D L. Interpretation of Weighted-Least-Squares Gas Path Analysis Results.ASME Journal of Engineering for Gas Turbines and Power,2003,125(3):624-633.
[65] Stamatis A. Mathioudakis K. Papailiou K D. Adaptive Simulation of Gas TurbinePerformance. ASME Journal of Engineering for Gas Turbines and Power,1990,112(2):168-175.
[66] Aretakis N. Mathioudakis K. Stamatic A. Nonlinear Engine Component FaultDiagnosis From a Limited Number of Measurements Using a Combinatorial Approach.ASME Journal of Engineering for Gas Turbines and Power,2003,125(2):642-650.
[67] Li Y G. Gas Turbine Performance and Health Status Estimation Using Adaptive GasPath Analysis. ASME Journal of Engineering for Gas Turbines and Power,2010,125(4),041701.
[68] Simon D L. Gary S. Optimal Tuner Selection for Kalman Filter-Based Aircraft EnginePerformance Estimation. ASME Journal of Engineering for Gas Turbines and Power,2010,132(3),031601.
[69] Sugiyama N. System Identification of Jet Engines. ASME Journal of Engineering forGas Turbines and Power,2000,122(1):19-26.
[70] Simon D, Simon D L. Aircraft Turbofan Engine Health Estimation Using ConstrainedKalman Filtering. ASME Journal of Engineering for Gas Turbines and Power,2005,127(2):323-328.
[71] Litt J S. An Optimal Orthogonal Decomposition Method for Kalman Filter-BasedTurbofan Engine Thrust Estimation. ASME Journal of Engineering for Gas Turbinesand Power,2008,130(1),011601.
[72] Borguet S, Léonard O. A Generalized Likelihood Ratio Test for Adaptive Gas TurbinePerformance Monitoring. ASME Journal of Engineering for Gas Turbines and Power,2009,131(1),011601.
[73] Lee Y K, Mavris D N, Volovoi V V, etc. A fault diagnosis method for industrial gasturbines using Bayesian data analysis. ASME Journal of Engineering for Gas Turbinesand Power,2010,132(4),041602.
[74] Romessis C, Mathioudakis K. Bayesian Network Approach for Gas Path FaultDiagnosis. ASME Journal of Engineering for Gas Turbines and Power,2006,128(1):64-72.
[75] Kestner B K, Lee Y K, Voleti Gautham, etc. Diagnostics of Highly DegradedIndustrial Gas Turbines Using Bayesian Networks. ASME2011, GT-2011-45249.
[76] Sampath S, Singh R. An Integrated Fault Diagnostics Model Using Genetic Algorithmand Neural Networks. ASME Journal of Engineering for Gas Turbines and Power,2006,128(1):49-56.
[77] Li Y G. An Genetic Algorithm Approach to Estimate Performance Status of GasTurbines. ASME2008, GT-2008-50175.
[78] Antonisse J. A new interpretation of schema notation that overturns the binaryencoding constraint. Proceedings of the Third InternationalConference on GeneticAlgorithms,1989.
[79] Bettocchi R, Pinelli M, Spina P R, etc. Artificial Intelligence for the Diagnostics ofGas Turbines-Part I: Neural Network Approach. ASME Journal of Engineering forGas Turbines and Power,2007,129(3):711-719.
[80] Bettocchi R, Pinelli M, Spina P R, etc. Artificial Intelligence for the Diagnostics ofGas Turbines-Part I I: Neuro-Fuzzy Approach. ASME Journal of Engineering for GasTurbines and Power,2007,129(3):720-729.
[81] Lu P J, Zhang M C, Hsu T C, Zhang J. An Evaluation of Engine Faults DiagnosticsUsing Artificial Neural Networks. ASME Journal of Engineering for Gas Turbines andPower,2001,123(2):340-346.
[82] Romesis C, Mathioudakis K. Setting Up of a Probabilistic Neural Network for SensorFault Detection Including Operation With Component Faults. ASME Journal ofEngineering for Gas Turbines and Power,2003,125(3):64-641.
[83] Lee S M, Choi W J, Roh T S, etc. A Study on Separate Learning Algorithm UsingSupport Vector Machine for Defect Diagnostics of Gas Turbine Engine. Journal ofMechanical Science and Technology,2008,22(12):2489-2497.
[84] Lee S M, Roh T S, Choi W J. Defect diagnostics of SUAV gas turbine engine usinghybrid SVM-artificial neural network method. Journal of Mechanical Science andTechnology,2009,23(2):559-568.
[85]尉询楷,陆波,汪诚,等.支持向量机在航空发动机故障诊断中的应用.航空动力学报,2004,19(6):844-848.
[86]徐启华,师军.基于支持向量机的航空发动机故障诊断.航空动力学报,2005,20(2):298-303.
[87] Dunia R, Qin S J, Edgar T F. Identification of faulty sensors using principalcomponent analysis. Process Systems Engineering,1996,42(10):2797-2812.
[88] Gomez G, Lendasse A. Statistical fault isolation with principal component analysis.IFAC safe process2000.
[89] Borguet S, Léonard O. Coupling principal component analysis and Kalman filteringalgorithms for on-line aircraft engine diagnostics. Control Engineering Practice,2009,17(4):494-502.
[90] Ganguli R. Application of Fuzzy Logic for Fault Isolation of Jet Engines. ASMEJournal of Engineering for Gas Turbines and Power,2003,125(3):617-623.
[91] Martis D. Fuzzy Logic Estimation Applied to Newton Methods for Gas Turbines.ASME Journal of Engineering for Gas Turbines and Power,2007,129(1):88-96.
[92] Eustace R W. A Real-World Application of Fuzzy Logic and Influence Coefficientsfor Gas Turine Performance Diagnostics. ASME Journal of Engineering for GasTurbines and Power,2008,130(6),061601.
[93] Doel D L. The Role of Expert Systems in Commercial Gas Turbine Engine Monitoring.ASME1990,90-GT-374.
[94] Torella G. Expert Systems for the Trouble-Shooting and the Diagnostics of Engines.AIAA Paper, AIAA-92-3327.
[95] DePold H R, Gass F D. The Application of Expert Systems and Neural Networks toGas Turbine Prognostics and Diagnostics. ASME Journal of Engineering for GasTurbines and Power,1999,121(4):607-612.
[96] Li Y G. Performance-analysis-based gas turbine diagnostics: A review. Proceedings ofthe Institution of Mechanical Engineers, Part A: Journal of Power and Energy.2002,216:363-377.
[97] Marinai L, Probert D, Singh R. Prospects for aero gas turbine diagnosis: a review.2004,79(1):109-126.
[98] Donat W, Choi K, An W, etc. Data Visualization,Date Reduction and Classifier Fusionfor Intelligent Fault Diagnosis in Gas Turbine Engines. ASME Journal of Engineeringfor Gas Turbines and Power,2008,130(4),041602.
[99] Brotherton T, Volponi A, Luppold R, etc. eSTORM:Enhanced Self-tuning On-boardReal-time Engine Model. IEEE Aerospace Conference,2003,7:3075-3086.
[100] Volponi A J. Foundation of Gas Path Analysis (Part I and II)(von Karman InstituteLecture Series). von Karman Institute,2003, Rhode-Saint-Genèse, Belgium.
[101] Stamatis A, Papailiou K D. Discrete operating condition gas path analysis. EngineCondition Monitoring-Technology and Experience,1988.
[102] Stamatis A, Mathioudakis K, Berios G, etc. Jet engine fault detection with discreteoperating points gas path analysis. Journal of Propulsion and Power.1991,7(6):1043-1048.
[103] Gulati A, Zedda M, Singh R. Gas Turbine Engine and Sensor Multiple Operating PointAnalysis Using Optimization Techniques. AIAA Paper, AIAA-2000-3716.
[104] Escher P C. An objective-oriented gas turbine path analysis computer program forgeneral applications. United Kingdom: Cranfield University,1995.
[105] Kamboukos P, Mathioudakis K. Comparison of Linear and Nonlinear Gas TurbinePerformance Diagnostics. ASME Journal of Engineering for Gas Turbines and Power,2005,127(1):49-56.
[106] Kalman R E. A New Approach to Linear Filtering and Prediction Problems. Journal ofBasic Engineering.1960,82(1):35-45.
[107] Provost, M. COMPASS: a generalized ground-based monitoring system.AGARD-CP-449, Engine Condition Monitoring Technology and Experience,1988.
[108] Luppold R, Roman J, Gallops G, etc. Estimating in-flight engine performancevariations using Kalman filter concepts. AIAA, ASME, SAE, and ASEE, JointPropulsion Conference,25th, Monterey, CA, USA,1989.
[109] Heather H L. A Simulation Study of Turbofan Engine Deterioration Estimation UsingKalman Filtering Techniques. NASA, Technical Memorandum,104233.
[110] Guo T H, Merrill W. A Distributed Fault-Detection and Diagnosis System UsingOnline Parameter Estimation. NASA, Technical Memorandum,104433.
[111] Simon D, Simon D L. Constrained Kalman Filtering Via Density Function Truncationfor Turbofan Engine Health Estimation. NASA/TM-2006-214126.
[112] Simon D, Simon D L. Kalman Filtering with Inequality Constraints for TurbofanEngine Health Estimation. NASA/TM-2003-212111.
[113] Dewallef P, Leonard O. On-line Performance Monitoring and Engine DiagnosticUsing Robust Kalman Filtering Techniques. ASME,2003, GT-2003-38379.
[114] Borguet S, Dewallef P, etc. On-line Transient Engine Diagnostics in a KalmanFiltering Framework. ASME,2005, GT2005-68013.
[115] Espańa M D. On the Estimation Algorithm for Adaptive Performance Optimization ofTurbofan Engines. AIAA Paper, AIAA-93-1823.
[116] Palmer C A. Combining Bayesian belief networks with gas path analysis for test celldiagnostics and overhaul. ASME1998, GT-1998-168.
[117] Volponi A J, Brotherton T, Luppold R. Development of an Information Fusion Systemfor Engine Diagnostics and Health Management. NASA/TM-2004-212924.
[118] Dewallef P, Romessis C, L onard O. Combining Classification Techniques WithKalman Filters for Aircraft Engine Diagnostics. ASME Journal of Engineering for GasTurbines and Power,2006,128(2):281-287.
[119] Loboda I, Yepifanov S, Feldshteyn Y. A Generalized Fault Classification for GasTurbine Diagnostics at Steady States and Transients. ASME Journal of Engineeringfor Gas Turbines and Power,2007,127(4):977-985.
[120] Torella G, Lombardo, G. Neural Networks for the diagnostics of gas turbine engines.ASME,1996,96-TA-39.
[121] Kanelopoulos K, Stamatis A, Mathioudakis K. Incorporating neural networks into gasturbine performance diagnostics. ASME,1997,97-GT-35.
[122] Tang G Yates, C L, Chen D. Comparative study of two neural networks applied to jetengine fault diagnosis. AIAA Paper, AIAA-98-3549.
[123] Volponi A J, Depold H, Ganguli R, etc. The Use of Kalman Filter and Neural Networkmethodologies in Gas Turbine Performance Diagnostics-A Comparative Study. ASMEJournal of Engineering for Gas Turbines and Power,2003,125(4):917-924.
[124]郝英.基于智能技术的民航发动机故障诊断和寿命预测研究.南京,南京航空航天大学,2006.
[125] Roemer M J. Engine Health Monitoring System for Advanced Diagnostic Monitoringfor Gas Turbine Engines. AFRL-PR-WP-TR-1998-2120,1999.
[126] Vapnic V. The Nature of Statistical Learning. New York, Springer,1998.
[127]蒲星星,刘尚明,张文杰,等.一种重型燃气轮机通用模型的建立.热力透平,2012,14(1):7-12.
[128] Bücker, D., Span, R., and Wagner, W. Thermodynamic Property Models for moist Airand Combustion Gases. ASME Journal of Engineering for Gas Turbines and Power,2003,125(1):374-384.
[129] Camporeale, S M., Fortunato, B., and Mastrovito, M. A Modular Code for Real TimeDynamic Simulation of Gas Turbines in Simulink. ASME Journal of Engineering forGas Turbines and Power,2006,128(3):506-517.
[130] Celis, C. Modeling of Variable Inlet Guide Vanes Affects on a One Shaft IndustrialGas Turbine Used in a Combined Cycle Application. ASME,2008, GT2008-50076.
[131]张文杰,刘尚明,蒲星星,等.基于Simulink的单轴重型燃气轮机启动过程仿真研究.燃气轮机技术,2012,25(1):27-32.
[132]蒲星星,刘尚明,周媛,等.重型燃气轮机分段线性化状态空间模型的建立.热力透平,2010,39(4):221-225.
[133] Stamatis, M., Mathioudakis, K., and Papailiou, K. Optimal Measurement and HealthIndex Selection for Gas Turbine Performance Status and Fault Diagnosis. ASMEJournal of Engineering for Gas Turbines and Power,1992,114(2):209-216.
[134] Mathioudakis, K., and Kamboukos, Ph. Assessment of the Effectiveness of Gas PathDiagnostic Schemes. ASME Journal of Engineering for Gas Turbines and Power,2006,128(1):57-63.
[135] McCusker, J. R., and Danai, K. Measurement Selection for Engine Transients byParameter Signatures. ASME Journal of Engineering for Gas Turbines and Power,2010,132(12):121601-1-121601-10.
[136] Mushini, R., and Simon, D. On optimization of sensor selection for aircraft gas turbineengines. Proceeding of18th International Conference on Systems Engineering,2005,9-18.
[137] Sowers, T. S., Kopasakis, G., and Simon, D. L. Application of the Systematic SensorSelection Strategy for Turbofan Engine Diagnostics. ASME,2008, GT2008-50525.
[138] Z. Chen. Local Observability and Its Application to Multiple Measurement Estimation.1991, IEEE Transactions on Industrial Electronics,38(6):491-496.
[139] Provost, M. J. The Use of Optimal Estimation Techniques in the Analysis of GasTurbines. Cranfield University,1994.
[140] Borguet, S., and Leonard, O. The Fisher Information Matrix as a Relevant Tool forSensor Selection in Engine Health Monitoring. International Journal of RotatingMachinery,2008, p.784749.
[141] Yim, J. R. Autonomous Spacecraft Orbit Navigation. Texas A&M University,2002.
[142] Ham, F. M., and Brown, R. G.. Observability, Eigenvalues, and Kalman Filtering.IEEE Transactions on Aerospace and Electronic Systems,1983, AES19(2):269-273.
[143] Hong, S., Chun, H. H., Kwon, S. H., and Lee, M. H. Observability Measures and TheirApplication to GPS/INS. IEEE Transactions on Vehicular Technology,2008,57(1):97-106.
[144]范炜,李勇.基于奇异值分解的航天器自主导航系统能观度分析.航天控制,2009,27(2):50-55.
[145] Pinelli, M., and Spina, P. Gas turbine field performance determination: sources ofuncertainties. ASME Journal of Engineering for Gas Turbines and Power,2002,124(1):155-160.
[146] Mehra, R K. Approaches to adaptive filtering. IEEE Transactions on AutomaticControl,1972,17(5):693-698.
[147] Alspach, D. L. A parallel filtering algorithm for linear systems with unknowntime-varying noise statistics. IEEE Transactions on Automatic Control,1974,19(5),552-556.
[148] Kashyap, R. L. Maximum likelihood identification of stochastic linear systems. IEEETransactions on Automatic Control,1970,15(1):25-34.
[149] Mehra, R K. On the identification of variances and adaptive Kalman filtering. IEEETransactions on Automatic Control,1970,15(2):175-184.
[150] Odelson, B., Rajamani, M., and Rawlings, J. A New Autocovariance Least-SquaresMethod for Estimating Noise Covariances. Automatica,2006,42(2):303-308.
[151] Jazwinski, A. H. Adaptive Filtering. Automatica,1969,5(4):475-485.
[152] Léonard, O., Borguet, S., and Dewallef, P. Adaptive estimation algorithm for aircraftengine performance monitoring. Journal of Propulsion and Power,2008,24(4):763-769.
[153] Willsky, A., and Jones, H. A Generalized Likelihood Ratio Approach to StateEstimation in Linear Systems Subject to Abrupt Changes. Proceedings of the IEEEConference on Decision and Control, Phoenix, AZ, November,1974,846-853.
[154] Chetouani, Y. Application of the Generalized Likelihood Ratio Test for DetectingChanges in a Chemical Reactor. Process Safety and Environmental Protection,2006,84(5):371-377.
[155] Borguet, S., and Léonard, O. Assessment of an Anomaly Detector for Jet EngineHealth Monitoring. International Journal of Rotating Machinery,2011,942576.
[156]周东华,叶银忠.现代故障诊断与容错控制,北京,清华大学出版社,2000.
[157]周东华,孙优贤.控制系统的故障检测与诊断技术,北京,清华大学出版社,1994.
[158] Zhou, D H., and Frank, P M. Strong tracking filtering of nonlinear time-varyingstochastic systems with coloured noise: application to parameter estimation andempirical robustness analysis. International Journal of Control,1996,65(2):295-307.
[159] Lipowsky, H., Staudacher, S., Bauer, M., and Schmidt, K. J. Application of BayesianForecasting to Change Detection and Prognosis of Gas Turbine Performance. ASMEJournal of Engineering for Gas Turbines and Power,2010,132(3),031602.
[160] Donoho, D. L. Compressed sensing. IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[161] Yang, A. Y., Wright, J., Ma, Y., and Sastry, S. S. Feature selection in face recognition:A sparse representation perspective. UC Berkeley Tech Report UCB/EECS-2007-99.
[162] Zhang, B., Karray, F., Li, Q., and Zhang, L. Sparse Representation Classifier formicroaneurysm detection and retinal blood vessel extraction. Information Sciences,2012,200(1):78-90.
[163] Borguet, S., and Léonard, O. A sparse estimation approach to fault isolation. ASMEJournal of Engineering for Gas Turbines and Power,2010,132(2),021601.
[164] Borguet, S., and Léonard, O. Constrained sparse estimation for improved faultisolation. ASME Journal of Engineering for Gas Turbines and Power,2011,133(12),121602.
[165] Fuchs, J. J. On Sparse Representations in Arbitrary Redundant Bases. IEEETransactions on Information Theory,2004,50(6):1341–1344.
[166] Fuchs, J. J. Recovery of Exact Sparse Representations in the Presence of Noise. IEEEInternational Conference on Acoustics, Speech, and Signal Processing,2004,2:533-536.
[167] Ji, S., Xue, Y., and Carin, L. Bayesian compressive sensing. IEEE Transactions onSignal Processing,2008,56(6):2346-2356.
[168] Wipf, D., and Rao, B.-norm minimization for basis selection. In Advances inNeural Information Processing Systems (NIPS17),2005.
[169] Wipf, D., and Rao, B. Comparing the effects of different weight distributions onfinding sparse representations. In Advances in Neural Information Processing Systems(NIPS18),2006.
[170] Tipping, M. E. Sparse Bayesian learning and the relevance vector machine. Journal ofMachine Learning Research,2001,1:211-244.
[171] Wipf, D. P., and Rao, B. D. Sparse Bayesian learning for basis selection. IEEETransactions on Signal Processing,2004,52(8):2153-2164.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |