发动机试验传感器数据证实的软计算方法与系统实现研究
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摘要
本文针对发动机试验领域对传感器数据证实技术的迫切需求,研究传感器数据证实的方法与应用系统实现的关键技术。
针对试验数据中脉冲型噪声去除问题,设计了中心加权等幂中值滤波器及其快速实现算法。设计了模拟发动机故障、性能退化和瞬变工况并包含野值与随机白噪声的传感器信号,测试了中值滤波器保持信号真实突变特征并去除脉冲噪声的性能及计算效率,将其应用于涡轮试验数据预处理取得良好效果。
针对线性关联多通道信号偏差检测问题,发展了主元—残差子空间方法。导出了偏差幅值与子空间参数对检测统计量影响的定量关系,发展了利用异常数据在多尺度分解下的统计量相对变化特征检测小幅值偏差的多尺度主元—残差子空间方法。应用于涡轮试验数据分析,表明该方法可减小偏差检测滞后性并降低偏差检测虚警率。
针对具有非线性和时变性关系的涡轮试验多通道信号偏差检测问题,建立了自关联神经网络估计器和预报器方法。分析了自关联神经网络结构及输入—输出参数与数据内在物理关联性之间的关系,研究提出了不同输入参数的涡轮试验传感器数据估计器和预报器,可实现偏差检测、分离及数据重构。
针对多源证据融合问题,建立了贝叶斯信度网络在发动机及其部件试验传感器数据证实中的应用方法,涉及传感器状态和检验关系式不确定性信息表达方法,根据传感器与检验关系式集合自动建立贝叶斯信度网络、计算可信度概率及更新网络的算法。分析了在线证实的实时性问题,给出了贝叶斯信度网络建立原则。
针对高压涡轮试验数据证实系统开发需要,建立了流体和机械运动参数之间的稳态关联方程,分析了基于物理模型的解析冗余方法可用性,给出了时域滑动数据窗相关方法在涡轮试验数据证实中的应用方案。
解决了传感器数据证实系统涉及的检验关系式数目、残差阈值、单周期决策逻辑、多周期决策策略、系统可放大性等问题;给出了传感器选择、关系式建立途径及系统调试方法,设计实现了传感器数据证实网络自动生成系统和实时运行内核。建立了组合解析冗余、统计相关检验与贝叶斯信度网络融合证据的传感器数据证实方案,实现了高压涡轮试验传感器数据证实系统。该系统具有事后证实功能,测试表明系统具有实用价值。
Sensor data validation is severely concerned by engineers of engine test domain, which calls forth the study on methods and system realization of sensor data validation by soft-computing technique for engine tests in this paper.
A center weighted idempotent median (CWIM) filter was introduced to removing high-amplitude impulsive noise in signals of gas turbine test, and a fast algorithm for the specific CWIM filter was devised. The filtering function and algorithmic efficiency were evaluated using a test signal, which representing the sensor response to engine abrupt faults, linear deterioration and/or regime transients, while contaminated with random noise and high-amplitude impulsive noise. Results confirmed that median filter is good at removing impulsive noise while preserving real steep edges in signals, also expected effect was obtained when applied to gas turbine test data.
A subspace model based approach was presented for detection of errors in signals with linear correlation. The normal subspace model was generated through principal component analysis (PCA), and statistics in conventional principal component subspace and residual subspace were quantitatively connected with error magnitude and subspace characteristics. The characteristics of statistics varying with data faults development in multi-scale PCA was analyzed, and a multi-scale subspace based method was developed for detection of small errors. The time lag of error detection was shortened, and false alarm rate was reduced when applied to practical gas turbine test data.
A neural network model based approach was presented to handle the bias detection and correction problem with data correlated by nonlinear and time-varying relations in gas turbine test. A special architecture of the auto-associative neural network was defined with different input and output parameters. Novel estimators and predictors based on auto-associative neural network were devised and evaluated with practical test data. A scheme integrating operations of error detection, fault isolation and data reconstruction was presented based on auto-associative neural network.
A method for fusing evidence information using Bayesian belief network was introduced into sensor data validation. Uncertainty expression of sensor state and relations in engine and its components test were defined. The algorithms for automatic generation of the Bayesian belief network files, belief probability calculation and network update after a abruption of one faulty sensor were developed. Real-time running feasibility was analyzed, and criteria for Bayesian belief network evaluation was presented.
The mathematical models for fluid and mechanical characteristics of high pressure gas turbine test system in steady regimes were developed. The effectiveness of analytical redundancy technique based on first principles models was evaluated. A scheme based on moving window technique that continuously computes auto-correlation function of samples of sensor data in time domain was devised.
A solution for validating gas turbine test sensor data was presented, which answered critical problems about the construction of a data validation system, including check relation number, residual thresholds, single cycle decision logic, multi-cycle decision strategy, system scaleable capacity to any sensor set, etc. The methods for sensor selection, relations definition and system test were presented in detail. A sensor validation network development system and a real-time kernel was developed in software. A prototype system was realized, which well demonstrated the versatility and effectiveness for post validation of practical high pressure gas turbine test data.
针对试验数据中脉冲型噪声去除问题,设计了中心加权等幂中值滤波器及其快速实现算法。设计了模拟发动机故障、性能退化和瞬变工况并包含野值与随机白噪声的传感器信号,测试了中值滤波器保持信号真实突变特征并去除脉冲噪声的性能及计算效率,将其应用于涡轮试验数据预处理取得良好效果。
针对线性关联多通道信号偏差检测问题,发展了主元—残差子空间方法。导出了偏差幅值与子空间参数对检测统计量影响的定量关系,发展了利用异常数据在多尺度分解下的统计量相对变化特征检测小幅值偏差的多尺度主元—残差子空间方法。应用于涡轮试验数据分析,表明该方法可减小偏差检测滞后性并降低偏差检测虚警率。
针对具有非线性和时变性关系的涡轮试验多通道信号偏差检测问题,建立了自关联神经网络估计器和预报器方法。分析了自关联神经网络结构及输入—输出参数与数据内在物理关联性之间的关系,研究提出了不同输入参数的涡轮试验传感器数据估计器和预报器,可实现偏差检测、分离及数据重构。
针对多源证据融合问题,建立了贝叶斯信度网络在发动机及其部件试验传感器数据证实中的应用方法,涉及传感器状态和检验关系式不确定性信息表达方法,根据传感器与检验关系式集合自动建立贝叶斯信度网络、计算可信度概率及更新网络的算法。分析了在线证实的实时性问题,给出了贝叶斯信度网络建立原则。
针对高压涡轮试验数据证实系统开发需要,建立了流体和机械运动参数之间的稳态关联方程,分析了基于物理模型的解析冗余方法可用性,给出了时域滑动数据窗相关方法在涡轮试验数据证实中的应用方案。
解决了传感器数据证实系统涉及的检验关系式数目、残差阈值、单周期决策逻辑、多周期决策策略、系统可放大性等问题;给出了传感器选择、关系式建立途径及系统调试方法,设计实现了传感器数据证实网络自动生成系统和实时运行内核。建立了组合解析冗余、统计相关检验与贝叶斯信度网络融合证据的传感器数据证实方案,实现了高压涡轮试验传感器数据证实系统。该系统具有事后证实功能,测试表明系统具有实用价值。
Sensor data validation is severely concerned by engineers of engine test domain, which calls forth the study on methods and system realization of sensor data validation by soft-computing technique for engine tests in this paper.
A center weighted idempotent median (CWIM) filter was introduced to removing high-amplitude impulsive noise in signals of gas turbine test, and a fast algorithm for the specific CWIM filter was devised. The filtering function and algorithmic efficiency were evaluated using a test signal, which representing the sensor response to engine abrupt faults, linear deterioration and/or regime transients, while contaminated with random noise and high-amplitude impulsive noise. Results confirmed that median filter is good at removing impulsive noise while preserving real steep edges in signals, also expected effect was obtained when applied to gas turbine test data.
A subspace model based approach was presented for detection of errors in signals with linear correlation. The normal subspace model was generated through principal component analysis (PCA), and statistics in conventional principal component subspace and residual subspace were quantitatively connected with error magnitude and subspace characteristics. The characteristics of statistics varying with data faults development in multi-scale PCA was analyzed, and a multi-scale subspace based method was developed for detection of small errors. The time lag of error detection was shortened, and false alarm rate was reduced when applied to practical gas turbine test data.
A neural network model based approach was presented to handle the bias detection and correction problem with data correlated by nonlinear and time-varying relations in gas turbine test. A special architecture of the auto-associative neural network was defined with different input and output parameters. Novel estimators and predictors based on auto-associative neural network were devised and evaluated with practical test data. A scheme integrating operations of error detection, fault isolation and data reconstruction was presented based on auto-associative neural network.
A method for fusing evidence information using Bayesian belief network was introduced into sensor data validation. Uncertainty expression of sensor state and relations in engine and its components test were defined. The algorithms for automatic generation of the Bayesian belief network files, belief probability calculation and network update after a abruption of one faulty sensor were developed. Real-time running feasibility was analyzed, and criteria for Bayesian belief network evaluation was presented.
The mathematical models for fluid and mechanical characteristics of high pressure gas turbine test system in steady regimes were developed. The effectiveness of analytical redundancy technique based on first principles models was evaluated. A scheme based on moving window technique that continuously computes auto-correlation function of samples of sensor data in time domain was devised.
A solution for validating gas turbine test sensor data was presented, which answered critical problems about the construction of a data validation system, including check relation number, residual thresholds, single cycle decision logic, multi-cycle decision strategy, system scaleable capacity to any sensor set, etc. The methods for sensor selection, relations definition and system test were presented in detail. A sensor validation network development system and a real-time kernel was developed in software. A prototype system was realized, which well demonstrated the versatility and effectiveness for post validation of practical high pressure gas turbine test data.
引文
[1]张育林,吴建军,朱恒伟,等.液体火箭发动机健康监控技术[M].长沙:国防科技大学出版社,1998.11.
[2]Jonathan S.Litt,Donald L.Simon,Sanjay Garg,et al.A survey of intelligent control and health management technologies for aircraft propulsion systems[R].NASA/TM-2005 -213622,May 2005.
[3]William A.Maul,Amy Chicatelli,Christopher E.Fulton,et al.Addressing the real- world challenges in the development of propulsion IVHM technology experiment (PITEX)[C].AIAA-2004-6361(NASA/CR-2005-213422),First Intelligent Systems Technical Conference,Chicago,Illinois,September 20-22,2004.
[4]Kevin J.Melcher,T.Shane Sowers,William A.Maul.Meeting the challenges of exploration systems:health management technologies for aerospace systems with emphasis on propulsion[R].NASA/TM-2005-214026,First International Forum on Integrated System Health Engineering and Management in Aerospace,Napa,California,November 7-10,2005.
[5]Santi L.Michael,Sowers T.Shane,Aguilar Robert.Optimal sensor selection for health monitoring systems[C].AIAA 2005-4485(NASA/TM-2005-213955),41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit,Tucson,Arizona,July 10-13,2005.
[6]Sanjay Garg.NASA Glenn research in controls and diagnostics for intelligent aerospace propulsion systems[R].NASA/TM-2005-214036,December,2005.
[7]Taniguchi M.H..Failure control techniques for the SSME,phase Ⅱ:final report[R].NASA CR-179231,1987.
[8]Meyer Claudia M.,William A.Maul..Development of life prediction capabilities for liquid propulsion rocket engines-task 3,sensor data validation and reconstruction,phase Ⅰ:system architecture study,final report[R].Aerojet Propulsion Division,Contract NAS 3-25883,March 1991.
[9]Bickmore T.W..A probabilistic approach to sensor data validation.AIAA 92-3163,28th Joint Propulsion Conference,Nashville,Tennessee,1992.
[10]Bickmore T.W..Real- time sensor data validation[R].NASA CR-195295,April,1994.
[11]Bickford R.L.,et al.Real-time flight data validation for rocket engines[C].AIAA 96- 2827,32nd Joint Propulsion Conference,Lake Buena Vista,Florida,1996.
[12]Bickford R.L.,et al.Real-time sensor data validation for autonomous flight control[C].AIAA 97-2901,33rd Joint Propulsion Conference,Seattle,Washington,1997.
[13]Bickford R.L.,Bickmore T.W.,Meyer C.M.,et al.Real-time flight data validation for propulsion systems[C].AIAA-98-5184,34th Joint Propulsion Conference, Cleveland,Ohio,July 1998.
[14]Herzog J.P.,Yue Y.Y.,Bickford B.L..Dynamic sensor data validation for reusable launch vehicle propulsion[C].AIAA-98-3604,34th Joint Propulsion Conference,Cleveland,Ohio,July 1998.
[15]Bickford R.L.,Bickmore T.W.,Meyer C.M.,et al.Real-time sensor data validation for space shuttle main engine telemetry monitoring[C].AIAA-99-2531,35th Joint Propulsion Conference and Exhibit,Los Angeles,California,June 1999.
[16]Sowers T.Shane,Santi L.Michael,Bickford Randall L..Performance evaluation of a data validation system[C].AIAA-2005-4486(NASA/CR-2005-213813),41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit,Tucson,Arizona,July 10-13,2005.
[17]Donald L.Simon,Sanjay Gang,Gary W.Hunter,et al.Sensor needs for control and health management of intelligent aircraft engines[C].GT2004-54324(NASA/TM-2004 -213202,ARL-TR-3251) ASME Turbo Expo 2004,Vienna,Austria,June 14-17,2004.
[18]Donald J.Malloy.Improved data validation and quality assurance in turbine engine test facilities[C].AIAA-93-2178,29th Joint Propulsion Conference and Exhibit,Monterey,CA,June 28-30,1993.
[19]Paul G.K.,Lanny L.H..A turbine engine real-time data validation and analysis system,generation 1.0[C].AIAA2000-3626,36th Joint Propulsion Conference and Exhibit,Huntsville,AL,July 2000.
[20]张健.航空发动机地面试验技术的近期发展及我们的对策[J].燃气涡轮试验与研究,2002(1).
[21]Chappell M.A.,Malloy D.J.,Feather B.K..Model-based test data validation[C].AEDC/PA 2000-255,HPC Success Stories at Arnold Engineering Development Center,Arnold AFB,TN.
[22]Basseville M..Detecting changes in signals and systems-a survey[J].Automatica,1988,24.
[23]Frank P.M..Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results[J].Automatica,1990,26(3):459-474.
[24]Isermann R..Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper[J].Automatica,1994,29(4).
[25]Frank P.M.,Ding X..Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis[J].Automatica,1994,30(5).
[26]Sail M.,Guan Y..A new approach to robust fault detection and identification[J].IEEE Trans.on Aerospace and Electronic Systems,1993,29(3).
[27]Frank P.M..Enhancement of robustness in observer-based fault detection[J].Int.J.Control,1994,54(4).
[28]Meyer Claudia M.,William A.Maul.The application of neural networks to the SSME start-up transient[R]. NASA CR-187138(AIAA 91-2350), June 1991.
[29] William A. Maul.. Multi-sensor analysis techniques for SSME safety monitoring[C]. AIAA90-1990, 26th Joint Propulsion Conference and Exhibit,Huntsville, AL, July 1990.
[30] Cost T. L., Hofmann M. O.. Engine data interpretation system[R]. N91-20205,July, 1991.
[31] June F. Z.. The development of a post-test diagnostic system for rocket engines[C].AIAA 91-2528,1991.
[32] Matijevic Z.. A pattern-recognition based expert system: a prototype for fault diagnosis in a liquid propellant rocket engine[C]. IAF-92-0676, Washington DC, 1992.
[33] Saravanan, Duyar A., Merrill W. C., et al. Identification of space shuttle main engine dynamic models from firing data[C]. AIAA-92-3317,28th Joint Propulsion Conference and Exhibit, Nashville, TN, July 6-8, 1992.
[34] Merrill W. C., DeLaat J. C., Bruton W.. Advanced detection, isolation, and accommo- dation of sensor failures: real-time evaluation[J]. AIAA J. of Guidance,Control and Dynamics, 1988,11(6).
[35] Merrill W.C., DeLaat J.C., Abdelwahab M.. Turbofan engine demonstration of sensor failure detection.[J] AIAA J. of Guidance, Control and Dynamics, 1991,14(2):337-349.
[36] Duyar A. Eldem V., Merrill W. C, et al. Fault detection and diagnosis in propulsion systems: a fault parameter estimation approach[J]. AIAA J. of Guidance,Control and Dynamics, 1994,17(1):104-108.
[37] Wheeler K. R., et al. Space shuttle main engine sensor modeling using radial-basis -function neural networks[J]. J. of Spacecraft and Rockets, 1994,31(6).
[38] Neppach C. D., et al. Sensor failure detection, identification and accommodation in a system without sensor redundancy[C], AIAA 95-0011.
[39] Brown K. K., Coleman H. W.. Enhancing rocket engine test analysis and performance models with the incorporation of uncertainties[C]. AIAA 95-3073.
[40] Richard D. S.. F-111/TE30 engine monitoring system: a fusion of past, present and future[C]. IEEE AUTOTESTCON,1989.
[41] Aimin H.. A theory of measurement in diagnosis from first principles[J]. Artificial Intelligence, 1994, 65.
[42] Roemer M. J., Ghiocel D. M.. A probabilistic approach to the diagnosis of gas turbine engine faults[C]. Inter. COMADEM Congress, Tasmania, Australia, 1999.
[43] Link J., Yu-tsung Wang, Richard F.. ICEMS: intelligent condition-based engine/equipment management system[C]. 2001-GT-0015, Proceeding of ASME TURBO EXPO 2001, New Orleans, Louisiana, 2001.
[44] Kai Frank Goebel, Agogino A.. Fuzzy sensor validation and fusion for gas turbine power plants[EB/OL]. Mechanical Systems and Signal Processing, 2001CRD033.
[45] Jeffrey Hoffman, Kenneth Kimble. Development of real-time engine diagnostics tools at the Arnold Engineering Development Center engine test facility[C].AIAA2005-4336,41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Exhibit,10-13 July 2005,Tucson,Arizona.
[46]杨光.机电产品BIT系统传感层降虚警的理论与技术研究[D].长沙:国防科学技术大学,2003.12.
[47]张育林,李东旭.动态系统故障诊断理论与应用[M].长沙:国防科技大学出版社,1997.12.
[48]杨蔚华.航空发动机建模及故障诊断[D].南京:南京航空航天大学,2000.03.
[49]于达仁,毛志伟,徐基豫.基于信息冗余的DEH系统传感器故障检测[J].动力工程,1996(6)
[50]钮永胜,赵新民.基于神经网络在线建模的非线性动态系统中传感器故障检测方法[J].宇航学报,1998(1).
[51]张彦铎,姜兴渭.某时变系统传感器故障的诊断方法与仿真研究[J].传感器技术,1999(4).
[52]朱平,黄文虎,姜兴渭等.基于模型的传感器故障诊断技术的研究[J].传感技术学报,1999(1).
[53]张晨,韩月秋,陶然.基于改进鲁棒自联想神经网络的传感器故障诊断新方法[J].仪器仪表学报,1999(2).
[54]黄向华,孙健国,依西亚索夫,等.基于自联想网络的发动机传感器解析余度技术[J].航空动力学报,1999(4).
[55]HUANG Xiang-hua(黄向华).Sensor Fault Diagnosis and Reconstruction of Engine Control System Based on Autoassociative Neural Network[J].Chinese Journal of Aeronautics,2004(1).
[56]叶志锋,孙健国.用自联想神经网络处理发动机测量参数[J].数据采集与处理,2003(2).
[57]吴祖堂,王群书,蒋庄德.传感器数据证实技术应用研究[J].仪器仪表学报,2004(2).
[58]房方,魏乐.传感器故障的神经网络信息融合诊断方法[J].传感技术学报,2000(4).
[59]王镛根,田霖.航空发动机量测系统的故障检测[J].航空动力学报,2000(1).
[60]李世玲,李治.基于小波变换的传感器故障检测技术研究[J].机车电传动,2000(4).
[61]张彦铎,姜兴渭,黄文虎.传感器故障诊断中的数据关联方法和应用[J].传感器技术,2001(5).
[62]沈毅,刘宜平,刘志言.模糊神经网络在多传感器故障检测与诊断中的应用[J].中国机械工程,2001(10).
[63]聂北刚,李初琴.传感器故障诊断实时监测系统设计的探讨[J].机械强度, 2001(3).
[64]张晓华,吴贵强.基于主元分析的传感器故障诊断[J].武汉理工大学学报,2002(2).
[65]荣吉利.基于模型的航天器在轨传感器故障诊断方法[J].兵工学报,2002(2).
[66]李凤保,杨黎明,张华,等.基于解析冗余的传感器故障检测、分离与辨识.传感器技术[J],2002(5).
[67]Lu P.J.(陆鹏举),Hsu,T.C.(徐自珍).Application of auto-associative neural network on gas-path sensor data validation[J].Journal of Propulsion and Power,2002,18(4):879-888.
[68]Tzu-Cheng Hsu(徐自珍).Engine gaspath sensor data validation and fault diagnosis using artificial neural networks[D].Tainan(台南):National Cheng Kung University(国立成功大学),October,2002.
[69]范景茹.以类神经网路发展发动机传感器即时讯号验证法[D].台南:国立成功大学,2003.7.
[70]Nounou Mohamed N.,Bakshi Bhavik R..On-line multi-scale filtering of random and gross errors without process models[R].Department of Chemical Engineering,The Ohio State University,Columbus,OH 43210,USA,1997.
[71]Bakshi B.R..Multiscale PCA with application to multivariate statistical process monitoring[J].J.of American Institute of Chemical Engineering,1998,(44):1596-1605.
[72]Aradhye Hrishikesh B.,Bakshi Bhavik R.,Strauss Ramon A.,et al.Multi-scale SPC using wavelets - theoretical analysis and properties[R].Department of Chemical Engineering,The Ohio State University,Columbus,OH 43210,USA,2003.
[73]Ungarala Sridhar,Bakshi Bhavik R..A multi-scale,bayesian and error-in-variables approach for linear dynamic data rectification[J].Computers and Chemical Engineering,2000,24:445-451.
[74]Kano Manabu,Nagao Koji,Hasebe Shinji,et al.Comparison of statistical process monitoring methods:application to the Eastman challenge problem[J].Computers and Chemical Engineering,2000,24:175-181.
[75]Kano Manabu,Nagao Koji,Hasebe Shinji,et al.Comparison of statistical process monitoring methods with applications to the Eastman challenge problem[J].Computers and Chemical Engineering,2002,26:161-174.
[76]Depold H.,Gass F.D..The application of expert systems and neural networks to gas turbine prognostics and diagnostics[J].J.of Engineering of Gas Turbine and Power,1999,121(4):607-612.
[77]Pinelli M.,Spina P.R..Gas path field performance determination:sources of uncer- tainties[J].J.of Engineering for Gas Turbines and Power,2002,124(1):155-160.
[78]Ganguli R..Data rectification and detection of trend shifts in jet engine gas path measurements using median filters and fuzzy logic[J].J.of Engineering for Gas Turbines and Power,2002,124(4):809-816.
[79] Ganguli, R.. Jet engine gas-path measurement filtering using center weighted idempotent median filters[J]. J. of Propulsion and Power, 2003,19(5):930-937.
[80] Manders E.J., Biswa G., Mosterman P.J., et al. Signal interpretation for monitoring and diagnosis: a cooling system test-bed[J]. IEEE Trans. on Instrumentation and Measurement, 2000, 49(3):503-508.
[81] Kramer M.A.. Auto-associative neural networks[J]. Computers & Chemical Engineering, 1992,16(4):313-328.
[82] William W. Hsieh. Nonlinear principal component analysis by neural networks[EB/OL]. http://www.ocgy.ubc.ca/projects/clim.pred. Appeared in Tellus,2001,53A:599-615.
[83] Kerr L.J., Nemec T.S., Gallops G.W.. Real-time estimation of gas turbine engine damage using a control-based Kalman filter algorithm[J]. J. of Engineering for Gas Turbine and Power, 1992,114(1): 187-195.
[84] Steve Gabel and Mike Elgersma. Flight test of propulsion monitoring and diagnostic system[R]. NASA/CR-2002-211485, March, 2002.
[85] Takahisa Kobayashi. Aircraft engine sensor/actuator/component fault diagnosis using a bank of Kalman filters[R]. NASA/CR-2003-212298, March 2003.
[86] Takahisa Kobayashi, Donald L. Simon. Evaluation of an enhanced bank of Kalman filters for in-flight aircraft engine sensor fault diagnostics[C]. GT2004-53640 (NASA/TM-2004- 213203, ARL-TR-3252), ASME Turbo Expo 2004, Vienna, Austria,June 14-17,2004.
[87] Menke T. E., Maybeck P. S.. Sensor/actuator failure detection in the Vista F-16 by multiple model adaptive estimation[J]. IEEE Trans. on Aerospace and Electronic Systems, 1995, 31(4):1218-1229.
[88] Mattern D., Jaw L., Guo T.H., et al. Simulation of an engine sensor validation scheme using an auto-associative neural network[C]. AIAA 97-2902, 33rd Joint Propulsion Confe- rence, Seattle, WA,1997.
[89] Duane L. Mattern, Link C. Jaw, Ten-Huei Guo, et al. Using neural networks for sensor validation[C]. AIAA-98-3547(NASA/TM-1998-208483), 34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Cleveland, Ohio, July 12-15,1998.
[90] Khargonekar P. P., Ting T. L.. Fault detection in the presence of modeling uncertainty[C]. IEEE Proceedings of the 32nd Conference on Decision and Control, pp.1716-1721, December 1993.
[91] Patton, R. J., Chen, J.. Robust fault detection of jet engine sensor systems using eigenstructure assignment[J]. J. of Guidance, Control and Dynamics, 1992, 15(6).
[92] Volponi A. J., DePold H., Ganguli R., et al. The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study[C].ASME Paper 2000-GT-547, Proceedings of ASME TURBOEXPO 2000, Munich,Germany.
[93]Hajiyev C.,Caliskan F..Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and robust Kalman filtering[J].Aerospace Science &Technology,2000(4):415-422.
[94]Moiler,J.C.,Litt.J.S.,Guo,T.-H..Neural network-based sensor validation for turboshaft engines[C].AIAA-98-3605,34th Joint Propulsion Conference and Exhibit,Cleveland,Ohio,July 12-15,1998.
[95]Marcello Napolitano,Younghwan An,Brad Seanor,et al.Application of a neural sensor validation scheme to actual Boeing 737-flight data[C].AIAAA-99-4236,35th Joint Propulsion Conference and Exhibit,Los Angeles,California,June 1999.
[96]Takahisa Kobayashi,Donald L.Simon.A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics[C].AIAA-2001-3763(NASA/TM- 2001-211088,ARL-TR- 1266),37th Joint Propulsion Conference and Exhibit,Salt Lake City,Utah,July 8-11,2001.
[97]Zhou Jiemin(周洁敏),Lin Gang,Gong Shu,et al.Application of multi-sensor data fusion based-on fuzzy neural network in rotating mechanical failure diagnosis[J].Transac- tions of Nanjing University of Aeronautics & Astronautics,2001(1).
[98]Sheng Wan,Giampiero Campa,Marcello Napolitano.On-line learning RBF neural networks for sensor validation[C].AIAA 2002-4996,AIAA Guidance,Navigation,and Control Conference and Exhibit,5-8 August 2002,Monterey,Califomia.
[99]Allan J.Volponi,Tom Brotherton,Robert Luppold,et al.Development of an informa- tion fusion system for engine diagnostics and health management[R].NASA/TM-2004 -212924,February,2004.
[100]Allan J.Volponi.Data fusion for enhanced aircraft engine prognostics and health management[R].NASA/CR-2005-214055,December 2005.
[101]Chittaro L.,Guida G.,Tasso C.et al.Functional and teleological knowledge in multi-modeling approach for reasoning about physical system:a case study in diagnosis[J].IEEE Trans.on System,Man and Cybernetics,1993,23(6):.
[102]何友,王国宏.多传感器信息融合及应用[M].北京:电子工业出版社,2000.11.
[103]Michael J.Roemer,Gregory J.Kacprzynski,Donald Malloy.An approach to adaptable sensor fault diagnostics for engines and test cells[C].AIAA 2002-4306,38th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit,7-10 July 2002,Indianapolis,Indiana.
[104]李俭川,陶俊勇,胡茑庆,等.基于贝叶斯网络的智能故障诊断方法[J].中国惯性技术学报,2002,10(4):24-28.
[105]李俭川.贝叶斯网络故障诊断与维修决策方法及应用研究[D].长沙:国防科学技术大学,2002.10.
[106]Litt J.,Kurtkaya M.,Duyar A..Sensor fault detection and diagnosis for a T700turboshaft engine[J].AIAA J.of Guidance,Control and Dynamics,1995,18(3): 640-642.
[107]张丽,陈志强,高文焕,等.均值加速的快速中值滤波算法[J].清华大学学报,2004,44(9):1157-1159.
[108]王海清,宋执环,李平.主元分析方法的故障可检测性研究[J].仪器仪表学报,2002,23(3):232-235.
[2]Jonathan S.Litt,Donald L.Simon,Sanjay Garg,et al.A survey of intelligent control and health management technologies for aircraft propulsion systems[R].NASA/TM-2005 -213622,May 2005.
[3]William A.Maul,Amy Chicatelli,Christopher E.Fulton,et al.Addressing the real- world challenges in the development of propulsion IVHM technology experiment (PITEX)[C].AIAA-2004-6361(NASA/CR-2005-213422),First Intelligent Systems Technical Conference,Chicago,Illinois,September 20-22,2004.
[4]Kevin J.Melcher,T.Shane Sowers,William A.Maul.Meeting the challenges of exploration systems:health management technologies for aerospace systems with emphasis on propulsion[R].NASA/TM-2005-214026,First International Forum on Integrated System Health Engineering and Management in Aerospace,Napa,California,November 7-10,2005.
[5]Santi L.Michael,Sowers T.Shane,Aguilar Robert.Optimal sensor selection for health monitoring systems[C].AIAA 2005-4485(NASA/TM-2005-213955),41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit,Tucson,Arizona,July 10-13,2005.
[6]Sanjay Garg.NASA Glenn research in controls and diagnostics for intelligent aerospace propulsion systems[R].NASA/TM-2005-214036,December,2005.
[7]Taniguchi M.H..Failure control techniques for the SSME,phase Ⅱ:final report[R].NASA CR-179231,1987.
[8]Meyer Claudia M.,William A.Maul..Development of life prediction capabilities for liquid propulsion rocket engines-task 3,sensor data validation and reconstruction,phase Ⅰ:system architecture study,final report[R].Aerojet Propulsion Division,Contract NAS 3-25883,March 1991.
[9]Bickmore T.W..A probabilistic approach to sensor data validation.AIAA 92-3163,28th Joint Propulsion Conference,Nashville,Tennessee,1992.
[10]Bickmore T.W..Real- time sensor data validation[R].NASA CR-195295,April,1994.
[11]Bickford R.L.,et al.Real-time flight data validation for rocket engines[C].AIAA 96- 2827,32nd Joint Propulsion Conference,Lake Buena Vista,Florida,1996.
[12]Bickford R.L.,et al.Real-time sensor data validation for autonomous flight control[C].AIAA 97-2901,33rd Joint Propulsion Conference,Seattle,Washington,1997.
[13]Bickford R.L.,Bickmore T.W.,Meyer C.M.,et al.Real-time flight data validation for propulsion systems[C].AIAA-98-5184,34th Joint Propulsion Conference, Cleveland,Ohio,July 1998.
[14]Herzog J.P.,Yue Y.Y.,Bickford B.L..Dynamic sensor data validation for reusable launch vehicle propulsion[C].AIAA-98-3604,34th Joint Propulsion Conference,Cleveland,Ohio,July 1998.
[15]Bickford R.L.,Bickmore T.W.,Meyer C.M.,et al.Real-time sensor data validation for space shuttle main engine telemetry monitoring[C].AIAA-99-2531,35th Joint Propulsion Conference and Exhibit,Los Angeles,California,June 1999.
[16]Sowers T.Shane,Santi L.Michael,Bickford Randall L..Performance evaluation of a data validation system[C].AIAA-2005-4486(NASA/CR-2005-213813),41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit,Tucson,Arizona,July 10-13,2005.
[17]Donald L.Simon,Sanjay Gang,Gary W.Hunter,et al.Sensor needs for control and health management of intelligent aircraft engines[C].GT2004-54324(NASA/TM-2004 -213202,ARL-TR-3251) ASME Turbo Expo 2004,Vienna,Austria,June 14-17,2004.
[18]Donald J.Malloy.Improved data validation and quality assurance in turbine engine test facilities[C].AIAA-93-2178,29th Joint Propulsion Conference and Exhibit,Monterey,CA,June 28-30,1993.
[19]Paul G.K.,Lanny L.H..A turbine engine real-time data validation and analysis system,generation 1.0[C].AIAA2000-3626,36th Joint Propulsion Conference and Exhibit,Huntsville,AL,July 2000.
[20]张健.航空发动机地面试验技术的近期发展及我们的对策[J].燃气涡轮试验与研究,2002(1).
[21]Chappell M.A.,Malloy D.J.,Feather B.K..Model-based test data validation[C].AEDC/PA 2000-255,HPC Success Stories at Arnold Engineering Development Center,Arnold AFB,TN.
[22]Basseville M..Detecting changes in signals and systems-a survey[J].Automatica,1988,24.
[23]Frank P.M..Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-a survey and some new results[J].Automatica,1990,26(3):459-474.
[24]Isermann R..Fault diagnosis of machines via parameter estimation and knowledge processing-tutorial paper[J].Automatica,1994,29(4).
[25]Frank P.M.,Ding X..Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis[J].Automatica,1994,30(5).
[26]Sail M.,Guan Y..A new approach to robust fault detection and identification[J].IEEE Trans.on Aerospace and Electronic Systems,1993,29(3).
[27]Frank P.M..Enhancement of robustness in observer-based fault detection[J].Int.J.Control,1994,54(4).
[28]Meyer Claudia M.,William A.Maul.The application of neural networks to the SSME start-up transient[R]. NASA CR-187138(AIAA 91-2350), June 1991.
[29] William A. Maul.. Multi-sensor analysis techniques for SSME safety monitoring[C]. AIAA90-1990, 26th Joint Propulsion Conference and Exhibit,Huntsville, AL, July 1990.
[30] Cost T. L., Hofmann M. O.. Engine data interpretation system[R]. N91-20205,July, 1991.
[31] June F. Z.. The development of a post-test diagnostic system for rocket engines[C].AIAA 91-2528,1991.
[32] Matijevic Z.. A pattern-recognition based expert system: a prototype for fault diagnosis in a liquid propellant rocket engine[C]. IAF-92-0676, Washington DC, 1992.
[33] Saravanan, Duyar A., Merrill W. C., et al. Identification of space shuttle main engine dynamic models from firing data[C]. AIAA-92-3317,28th Joint Propulsion Conference and Exhibit, Nashville, TN, July 6-8, 1992.
[34] Merrill W. C., DeLaat J. C., Bruton W.. Advanced detection, isolation, and accommo- dation of sensor failures: real-time evaluation[J]. AIAA J. of Guidance,Control and Dynamics, 1988,11(6).
[35] Merrill W.C., DeLaat J.C., Abdelwahab M.. Turbofan engine demonstration of sensor failure detection.[J] AIAA J. of Guidance, Control and Dynamics, 1991,14(2):337-349.
[36] Duyar A. Eldem V., Merrill W. C, et al. Fault detection and diagnosis in propulsion systems: a fault parameter estimation approach[J]. AIAA J. of Guidance,Control and Dynamics, 1994,17(1):104-108.
[37] Wheeler K. R., et al. Space shuttle main engine sensor modeling using radial-basis -function neural networks[J]. J. of Spacecraft and Rockets, 1994,31(6).
[38] Neppach C. D., et al. Sensor failure detection, identification and accommodation in a system without sensor redundancy[C], AIAA 95-0011.
[39] Brown K. K., Coleman H. W.. Enhancing rocket engine test analysis and performance models with the incorporation of uncertainties[C]. AIAA 95-3073.
[40] Richard D. S.. F-111/TE30 engine monitoring system: a fusion of past, present and future[C]. IEEE AUTOTESTCON,1989.
[41] Aimin H.. A theory of measurement in diagnosis from first principles[J]. Artificial Intelligence, 1994, 65.
[42] Roemer M. J., Ghiocel D. M.. A probabilistic approach to the diagnosis of gas turbine engine faults[C]. Inter. COMADEM Congress, Tasmania, Australia, 1999.
[43] Link J., Yu-tsung Wang, Richard F.. ICEMS: intelligent condition-based engine/equipment management system[C]. 2001-GT-0015, Proceeding of ASME TURBO EXPO 2001, New Orleans, Louisiana, 2001.
[44] Kai Frank Goebel, Agogino A.. Fuzzy sensor validation and fusion for gas turbine power plants[EB/OL]. Mechanical Systems and Signal Processing, 2001CRD033.
[45] Jeffrey Hoffman, Kenneth Kimble. Development of real-time engine diagnostics tools at the Arnold Engineering Development Center engine test facility[C].AIAA2005-4336,41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Exhibit,10-13 July 2005,Tucson,Arizona.
[46]杨光.机电产品BIT系统传感层降虚警的理论与技术研究[D].长沙:国防科学技术大学,2003.12.
[47]张育林,李东旭.动态系统故障诊断理论与应用[M].长沙:国防科技大学出版社,1997.12.
[48]杨蔚华.航空发动机建模及故障诊断[D].南京:南京航空航天大学,2000.03.
[49]于达仁,毛志伟,徐基豫.基于信息冗余的DEH系统传感器故障检测[J].动力工程,1996(6)
[50]钮永胜,赵新民.基于神经网络在线建模的非线性动态系统中传感器故障检测方法[J].宇航学报,1998(1).
[51]张彦铎,姜兴渭.某时变系统传感器故障的诊断方法与仿真研究[J].传感器技术,1999(4).
[52]朱平,黄文虎,姜兴渭等.基于模型的传感器故障诊断技术的研究[J].传感技术学报,1999(1).
[53]张晨,韩月秋,陶然.基于改进鲁棒自联想神经网络的传感器故障诊断新方法[J].仪器仪表学报,1999(2).
[54]黄向华,孙健国,依西亚索夫,等.基于自联想网络的发动机传感器解析余度技术[J].航空动力学报,1999(4).
[55]HUANG Xiang-hua(黄向华).Sensor Fault Diagnosis and Reconstruction of Engine Control System Based on Autoassociative Neural Network[J].Chinese Journal of Aeronautics,2004(1).
[56]叶志锋,孙健国.用自联想神经网络处理发动机测量参数[J].数据采集与处理,2003(2).
[57]吴祖堂,王群书,蒋庄德.传感器数据证实技术应用研究[J].仪器仪表学报,2004(2).
[58]房方,魏乐.传感器故障的神经网络信息融合诊断方法[J].传感技术学报,2000(4).
[59]王镛根,田霖.航空发动机量测系统的故障检测[J].航空动力学报,2000(1).
[60]李世玲,李治.基于小波变换的传感器故障检测技术研究[J].机车电传动,2000(4).
[61]张彦铎,姜兴渭,黄文虎.传感器故障诊断中的数据关联方法和应用[J].传感器技术,2001(5).
[62]沈毅,刘宜平,刘志言.模糊神经网络在多传感器故障检测与诊断中的应用[J].中国机械工程,2001(10).
[63]聂北刚,李初琴.传感器故障诊断实时监测系统设计的探讨[J].机械强度, 2001(3).
[64]张晓华,吴贵强.基于主元分析的传感器故障诊断[J].武汉理工大学学报,2002(2).
[65]荣吉利.基于模型的航天器在轨传感器故障诊断方法[J].兵工学报,2002(2).
[66]李凤保,杨黎明,张华,等.基于解析冗余的传感器故障检测、分离与辨识.传感器技术[J],2002(5).
[67]Lu P.J.(陆鹏举),Hsu,T.C.(徐自珍).Application of auto-associative neural network on gas-path sensor data validation[J].Journal of Propulsion and Power,2002,18(4):879-888.
[68]Tzu-Cheng Hsu(徐自珍).Engine gaspath sensor data validation and fault diagnosis using artificial neural networks[D].Tainan(台南):National Cheng Kung University(国立成功大学),October,2002.
[69]范景茹.以类神经网路发展发动机传感器即时讯号验证法[D].台南:国立成功大学,2003.7.
[70]Nounou Mohamed N.,Bakshi Bhavik R..On-line multi-scale filtering of random and gross errors without process models[R].Department of Chemical Engineering,The Ohio State University,Columbus,OH 43210,USA,1997.
[71]Bakshi B.R..Multiscale PCA with application to multivariate statistical process monitoring[J].J.of American Institute of Chemical Engineering,1998,(44):1596-1605.
[72]Aradhye Hrishikesh B.,Bakshi Bhavik R.,Strauss Ramon A.,et al.Multi-scale SPC using wavelets - theoretical analysis and properties[R].Department of Chemical Engineering,The Ohio State University,Columbus,OH 43210,USA,2003.
[73]Ungarala Sridhar,Bakshi Bhavik R..A multi-scale,bayesian and error-in-variables approach for linear dynamic data rectification[J].Computers and Chemical Engineering,2000,24:445-451.
[74]Kano Manabu,Nagao Koji,Hasebe Shinji,et al.Comparison of statistical process monitoring methods:application to the Eastman challenge problem[J].Computers and Chemical Engineering,2000,24:175-181.
[75]Kano Manabu,Nagao Koji,Hasebe Shinji,et al.Comparison of statistical process monitoring methods with applications to the Eastman challenge problem[J].Computers and Chemical Engineering,2002,26:161-174.
[76]Depold H.,Gass F.D..The application of expert systems and neural networks to gas turbine prognostics and diagnostics[J].J.of Engineering of Gas Turbine and Power,1999,121(4):607-612.
[77]Pinelli M.,Spina P.R..Gas path field performance determination:sources of uncer- tainties[J].J.of Engineering for Gas Turbines and Power,2002,124(1):155-160.
[78]Ganguli R..Data rectification and detection of trend shifts in jet engine gas path measurements using median filters and fuzzy logic[J].J.of Engineering for Gas Turbines and Power,2002,124(4):809-816.
[79] Ganguli, R.. Jet engine gas-path measurement filtering using center weighted idempotent median filters[J]. J. of Propulsion and Power, 2003,19(5):930-937.
[80] Manders E.J., Biswa G., Mosterman P.J., et al. Signal interpretation for monitoring and diagnosis: a cooling system test-bed[J]. IEEE Trans. on Instrumentation and Measurement, 2000, 49(3):503-508.
[81] Kramer M.A.. Auto-associative neural networks[J]. Computers & Chemical Engineering, 1992,16(4):313-328.
[82] William W. Hsieh. Nonlinear principal component analysis by neural networks[EB/OL]. http://www.ocgy.ubc.ca/projects/clim.pred. Appeared in Tellus,2001,53A:599-615.
[83] Kerr L.J., Nemec T.S., Gallops G.W.. Real-time estimation of gas turbine engine damage using a control-based Kalman filter algorithm[J]. J. of Engineering for Gas Turbine and Power, 1992,114(1): 187-195.
[84] Steve Gabel and Mike Elgersma. Flight test of propulsion monitoring and diagnostic system[R]. NASA/CR-2002-211485, March, 2002.
[85] Takahisa Kobayashi. Aircraft engine sensor/actuator/component fault diagnosis using a bank of Kalman filters[R]. NASA/CR-2003-212298, March 2003.
[86] Takahisa Kobayashi, Donald L. Simon. Evaluation of an enhanced bank of Kalman filters for in-flight aircraft engine sensor fault diagnostics[C]. GT2004-53640 (NASA/TM-2004- 213203, ARL-TR-3252), ASME Turbo Expo 2004, Vienna, Austria,June 14-17,2004.
[87] Menke T. E., Maybeck P. S.. Sensor/actuator failure detection in the Vista F-16 by multiple model adaptive estimation[J]. IEEE Trans. on Aerospace and Electronic Systems, 1995, 31(4):1218-1229.
[88] Mattern D., Jaw L., Guo T.H., et al. Simulation of an engine sensor validation scheme using an auto-associative neural network[C]. AIAA 97-2902, 33rd Joint Propulsion Confe- rence, Seattle, WA,1997.
[89] Duane L. Mattern, Link C. Jaw, Ten-Huei Guo, et al. Using neural networks for sensor validation[C]. AIAA-98-3547(NASA/TM-1998-208483), 34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Cleveland, Ohio, July 12-15,1998.
[90] Khargonekar P. P., Ting T. L.. Fault detection in the presence of modeling uncertainty[C]. IEEE Proceedings of the 32nd Conference on Decision and Control, pp.1716-1721, December 1993.
[91] Patton, R. J., Chen, J.. Robust fault detection of jet engine sensor systems using eigenstructure assignment[J]. J. of Guidance, Control and Dynamics, 1992, 15(6).
[92] Volponi A. J., DePold H., Ganguli R., et al. The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study[C].ASME Paper 2000-GT-547, Proceedings of ASME TURBOEXPO 2000, Munich,Germany.
[93]Hajiyev C.,Caliskan F..Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and robust Kalman filtering[J].Aerospace Science &Technology,2000(4):415-422.
[94]Moiler,J.C.,Litt.J.S.,Guo,T.-H..Neural network-based sensor validation for turboshaft engines[C].AIAA-98-3605,34th Joint Propulsion Conference and Exhibit,Cleveland,Ohio,July 12-15,1998.
[95]Marcello Napolitano,Younghwan An,Brad Seanor,et al.Application of a neural sensor validation scheme to actual Boeing 737-flight data[C].AIAAA-99-4236,35th Joint Propulsion Conference and Exhibit,Los Angeles,California,June 1999.
[96]Takahisa Kobayashi,Donald L.Simon.A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics[C].AIAA-2001-3763(NASA/TM- 2001-211088,ARL-TR- 1266),37th Joint Propulsion Conference and Exhibit,Salt Lake City,Utah,July 8-11,2001.
[97]Zhou Jiemin(周洁敏),Lin Gang,Gong Shu,et al.Application of multi-sensor data fusion based-on fuzzy neural network in rotating mechanical failure diagnosis[J].Transac- tions of Nanjing University of Aeronautics & Astronautics,2001(1).
[98]Sheng Wan,Giampiero Campa,Marcello Napolitano.On-line learning RBF neural networks for sensor validation[C].AIAA 2002-4996,AIAA Guidance,Navigation,and Control Conference and Exhibit,5-8 August 2002,Monterey,Califomia.
[99]Allan J.Volponi,Tom Brotherton,Robert Luppold,et al.Development of an informa- tion fusion system for engine diagnostics and health management[R].NASA/TM-2004 -212924,February,2004.
[100]Allan J.Volponi.Data fusion for enhanced aircraft engine prognostics and health management[R].NASA/CR-2005-214055,December 2005.
[101]Chittaro L.,Guida G.,Tasso C.et al.Functional and teleological knowledge in multi-modeling approach for reasoning about physical system:a case study in diagnosis[J].IEEE Trans.on System,Man and Cybernetics,1993,23(6):.
[102]何友,王国宏.多传感器信息融合及应用[M].北京:电子工业出版社,2000.11.
[103]Michael J.Roemer,Gregory J.Kacprzynski,Donald Malloy.An approach to adaptable sensor fault diagnostics for engines and test cells[C].AIAA 2002-4306,38th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit,7-10 July 2002,Indianapolis,Indiana.
[104]李俭川,陶俊勇,胡茑庆,等.基于贝叶斯网络的智能故障诊断方法[J].中国惯性技术学报,2002,10(4):24-28.
[105]李俭川.贝叶斯网络故障诊断与维修决策方法及应用研究[D].长沙:国防科学技术大学,2002.10.
[106]Litt J.,Kurtkaya M.,Duyar A..Sensor fault detection and diagnosis for a T700turboshaft engine[J].AIAA J.of Guidance,Control and Dynamics,1995,18(3): 640-642.
[107]张丽,陈志强,高文焕,等.均值加速的快速中值滤波算法[J].清华大学学报,2004,44(9):1157-1159.
[108]王海清,宋执环,李平.主元分析方法的故障可检测性研究[J].仪器仪表学报,2002,23(3):232-235.