面向单元体的航空发动机健康状态评估与预测方法研究
|
摘要
随着我国民航机队规模的不断扩大，民航安全形势和压力越来越严峻，新一代航空运输系统也对行业提出了“安全关口前移”和“持续安全”的发展目标，民航发动机的安全和成本效益问题是亟待研究的重大问题。国内航空产业领域，新一代国产军用和民用发动机均对发动机健康管理（EHM）提出了需求。因此，无论从我国民航业工程应用需要，还是从国产发动机EHM系统的研究开发的角度出发，亟需在EHM基础理论、关键技术等方面开展相关研究。本文在充分研究了EHM概念和技术架构、理清了关键技术的发展现状及趋势的基础上，结合国内在发动机健康管理工程实践中存在的问题与不足，以气路部件为对象，分别在气路部件状态监控、单元体健康状态评估及剩余寿命预测建模三个方面展开了深入研究，一方面可为提高我国民航发动机的健康管理水平提供可行的方法和技术，另一方面也可借鉴民航发动机健康管理领域成熟的技术和方法为国产发动机EHM系统的开发提供技术储备。论文主要工作如下：
（1）气路部件状态监控方面：
在挖掘现有的气路监控技术的潜力方面，针对性能参数偏差值离散度大、故障特征容易被噪声淹没的问题，提出了基于贝叶斯因子的性能参数偏差值序列监控方法，以从含有噪声的偏差值序列中及时识别异常，提前故障征兆发现的时机；针对“一般”基线模型难以准确反映实际运行环境下个体发动机的特性的问题，提出了基于多元状态估计技术的“个性化”基线模型的挖掘方法，依据发动机实测数据建立起的“个性化”基线模型能更准确的特定发动机的特性，因此，能够更准确地计算出性能参数的偏差量，有利于提前故障征兆的发现时机。在新型气路监测技术方面，把尾气静电信号作为一种新的气路状态参数，提出了基于燃油流量单参数的和基于多元状态估计技术的多参数尾气静电信号基线模型挖掘方法。在建立起尾气静电信号的基线模型后，通过实时监控尾气静电信号RMS值与基线值的偏差，实现对气路部件状态的实时监控，提前故障征兆的发现时机。
（2）面向单元体的气路部件健康状态评估方面：
研究了航线条件下基于非线性自适应性能模型的单元体健康状态评估方法。考虑到外场条件下可测气路参数少于待估计的健康参数以及测量噪声的影响，将复杂的健康参数的求解问题转换为一个寻优问题，提出了基于排除法的故障隔离—评估方法，通过排除健康单元体把待估计参数维数降低到低于可测气路参数；针对实际外场使用中各单元体不可避免的发生缓慢的性能退化的问题，提出了考虑性能缓慢退化情况下的单元体健康参数的估计方法，首先通过跟踪各单元体的缓慢性能退化，得到故障前各单元体的实际退化状态，进而在故障隔离与评估时考虑进其影响以提高单元体健康参数估计的精度。针对气路可测参数有限、信息源单一的问题，提出了基于贝叶斯网络多源诊断信息融合的气路分析技术：以常规气路可测参数为主，定性诊断信息借助故障模式先验概率表引进，而定量信息的引入则借助健康参数的先验分布实现，由贝叶斯网络实现信息融合，提高单元体健康参数估计的准确度和精度。
（3）健康状态与剩余寿命预测建模方法研究：
针对实际运行环境下个体系统的剩余寿命预测问题，提出了基于状态空间退化模型和贝叶斯估计理论的使用可靠性评估与剩余寿命预测方法。以发动机排气温度裕度作为表征气路部件性能衰退的退化参数，由线性高斯状态空间模型来描述性能退化轨迹，利用共轭先验贝叶斯推理估计并预测退化状态。状态空间模型区分带有噪声的观测量与系统真实的退化状态，更加符合实际情况。此外，状态空间模型不需要对退化轨迹做平稳性假设，这使得模型便于处理因维修、故障等因素而引起的性能突变。考虑到仅用单参数难以全面地表征系统健康状况的问题，提出了通过融合多个性能参数得到系统的健康指数来表征气路部件健康状态，进而建立基于系统健康指数的状态空间退化模型，用于预测其健康状态退化趋势及剩余寿命。针对关键件的裂纹扩展这一典型的失效模式，提出了物理失效模型和检测/监测数据相融合的剩余寿命预测方法，根据其物理失效模型建立起状态空间形式的裂纹扩展模型，借助贝叶斯理论融合外场检测/监测信息，通过不断融合新的观测信息可降低剩余寿命预测的不确定性，为关键件延寿或视情维修提供辅助决策。
With the rapid development of civil aviation industry, the civil aircraft engine safety andbenefit/cost issue become an urgent problem. The next-generation air transportation system isproposed with the targets of “checkpoints shifted front for safety” and “continued safety”. Thedomestically produced civil and military aero engine has a need on the Engine Health Management(EHM) technologies. So there is an urgent need to strengthen the research on the key technologies toimprove the EHM capability of the civil aircraft engines and to support the development anddeployment of the EHM system for the domestically produced aero engines. Based on the literaturesurvey and a full study of the concept and technical architecture of the EHM, considering theproblems in the practice, the main topics of the thesis focus on the gas path components conditionmonitoring, health status assessment as well as remaing useful life prognostics methods. On one sideit can provide technologies and methods for the improvement of the civil aircraft engine EHMcapability, on the other side it can provide experiences and mature technologies for the domesticallyproduced engine’s EHM system. The main topics of the thesis are as follows:
(1) Gas path components condition monitoring:
In order to detect the anomaly as soon as possible from the delta parameter parameters with largenoise and scatters, which maybe cover the fault signature, then to trigger a timely warning at early stageof the fault, a Bayesian factor-based method is proposed to monitor and analyze parameter delta series.Since the “generic” baseline model embedded in the OEM software cannot capture the characteristicsof the individual engine under the real operating conditions, the Multi State Estimation Technique(MSET) is proposed to build the individual baseline model for the specific engine, which can capturethe characteristics of the individual engine. Based on the individual baseline model, more accurate deltadata can be obtained which can advance the fault warning in time. In the application of the new gaspath condition monitoring techniques, the Exhaust Gas Electrostatic Monitoring Signal (EGEMS) isthought as a new gas path performance parameter, and two EGEMS baseline model building methodsare proposed. One is based on one parameter-the fuel flow rate, and the other one is based on multiparameters which are correlated using the MSET. Based on the developed baseline model, the deltabetween the real RMS value of the EGEMS and baseline value is monitored in real time to monitor thegas path component condition and to trigger a warning once some fault occures.
(2) Gas path components health assessment:
A study of in-field engine components health assessment techniques based on adaptive engineperformance model is carried out. Considering the fact that less measured performance parameters thanthe health parameters to estimate and the influence of the measurement noise, the health parametersestimation problem is changed into an optimization problem. An exclusive method based faultdetection and assessment framework is proposed, in which the healthy module is excluded one-by-one to reduce the number of the health parameters to estimate until they are less than the measuredparameters. Considering the fact that the gradual performance deterioration in field is inevitable, ahealth parameter estimation framework is proposed to incorporate the gradual performancedeterioration information. In this framework, the gradual performance deterioration is tracked to get thedegradation state of each module before the fault, then the information is incorporated when isolatingand assessment the fault to improve the health assessment results. A information fusion based gas pathanalysis framework is proposed to tackle the issue of lack of enough measured gas path parameters. Inthis framework, an information fusion mechanism based on the Bayesian network is developed toincorporate the the diagnosis information from multi sources, in which the qualitative information isincorporated by the fault mode prior probability table and the quantitative information is incorporatedby the prior distribution of the health parameter, then the information is fused using Bayesian rules toimprove the accuracy and precision of the estimation results of the health parameters.
(3) Modeling methods for remaining useful life prognostics:
The methods on remaining useful life prognosis for individual system under real operatingconditions are discussed in depth. The state space-based degradation model combined with Bayesianstate estimation theory is proposed for system remaining useful life prognostics and in-servicereliability estimation. The EGTM parameter is used as a degradation parameter to quantify thedegradation state of the engine. Then linear Gaussian state space model is adapted to describe thedegradation trajectory based on the observed EGTM data, and then the conjugate Bayesian inferenceis carried out to estimate the degradation state and further to make a prediction of the failure time. Thestate space based degradation model differentiates the noisy observation from the true degradationstate, which is closer to the actual case. The state space degradation model does not need to makestationarity assumption, so it can effectively manage the situation when there is a sudden change inthe health state due to fault or maintenance. Considering the problems that a single parameter cannotcharacterize the health state of a complex system, a fusion mechanism is developed to fuse multiparameters to get a health index to characterize the health state of the gas path component, based onwhich a state space degradation model is established to describe the degradation path and predict theremaining useful life. For the crack growth failure of the critical components, a fusion framework isproposed to integrate the damage monitoring data and physics of failure mechanism for remaininguseful life prediction. A state space based crack growth model is developed based the Paris crackgrowth model, then the damage monitoring data is integrated using the Bayesian rule. By integratingthe monitoring data the prognostics uncertainty can be continued reduced.
（1）气路部件状态监控方面：
在挖掘现有的气路监控技术的潜力方面，针对性能参数偏差值离散度大、故障特征容易被噪声淹没的问题，提出了基于贝叶斯因子的性能参数偏差值序列监控方法，以从含有噪声的偏差值序列中及时识别异常，提前故障征兆发现的时机；针对“一般”基线模型难以准确反映实际运行环境下个体发动机的特性的问题，提出了基于多元状态估计技术的“个性化”基线模型的挖掘方法，依据发动机实测数据建立起的“个性化”基线模型能更准确的特定发动机的特性，因此，能够更准确地计算出性能参数的偏差量，有利于提前故障征兆的发现时机。在新型气路监测技术方面，把尾气静电信号作为一种新的气路状态参数，提出了基于燃油流量单参数的和基于多元状态估计技术的多参数尾气静电信号基线模型挖掘方法。在建立起尾气静电信号的基线模型后，通过实时监控尾气静电信号RMS值与基线值的偏差，实现对气路部件状态的实时监控，提前故障征兆的发现时机。
（2）面向单元体的气路部件健康状态评估方面：
研究了航线条件下基于非线性自适应性能模型的单元体健康状态评估方法。考虑到外场条件下可测气路参数少于待估计的健康参数以及测量噪声的影响，将复杂的健康参数的求解问题转换为一个寻优问题，提出了基于排除法的故障隔离—评估方法，通过排除健康单元体把待估计参数维数降低到低于可测气路参数；针对实际外场使用中各单元体不可避免的发生缓慢的性能退化的问题，提出了考虑性能缓慢退化情况下的单元体健康参数的估计方法，首先通过跟踪各单元体的缓慢性能退化，得到故障前各单元体的实际退化状态，进而在故障隔离与评估时考虑进其影响以提高单元体健康参数估计的精度。针对气路可测参数有限、信息源单一的问题，提出了基于贝叶斯网络多源诊断信息融合的气路分析技术：以常规气路可测参数为主，定性诊断信息借助故障模式先验概率表引进，而定量信息的引入则借助健康参数的先验分布实现，由贝叶斯网络实现信息融合，提高单元体健康参数估计的准确度和精度。
（3）健康状态与剩余寿命预测建模方法研究：
针对实际运行环境下个体系统的剩余寿命预测问题，提出了基于状态空间退化模型和贝叶斯估计理论的使用可靠性评估与剩余寿命预测方法。以发动机排气温度裕度作为表征气路部件性能衰退的退化参数，由线性高斯状态空间模型来描述性能退化轨迹，利用共轭先验贝叶斯推理估计并预测退化状态。状态空间模型区分带有噪声的观测量与系统真实的退化状态，更加符合实际情况。此外，状态空间模型不需要对退化轨迹做平稳性假设，这使得模型便于处理因维修、故障等因素而引起的性能突变。考虑到仅用单参数难以全面地表征系统健康状况的问题，提出了通过融合多个性能参数得到系统的健康指数来表征气路部件健康状态，进而建立基于系统健康指数的状态空间退化模型，用于预测其健康状态退化趋势及剩余寿命。针对关键件的裂纹扩展这一典型的失效模式，提出了物理失效模型和检测/监测数据相融合的剩余寿命预测方法，根据其物理失效模型建立起状态空间形式的裂纹扩展模型，借助贝叶斯理论融合外场检测/监测信息，通过不断融合新的观测信息可降低剩余寿命预测的不确定性，为关键件延寿或视情维修提供辅助决策。
With the rapid development of civil aviation industry, the civil aircraft engine safety andbenefit/cost issue become an urgent problem. The next-generation air transportation system isproposed with the targets of “checkpoints shifted front for safety” and “continued safety”. Thedomestically produced civil and military aero engine has a need on the Engine Health Management(EHM) technologies. So there is an urgent need to strengthen the research on the key technologies toimprove the EHM capability of the civil aircraft engines and to support the development anddeployment of the EHM system for the domestically produced aero engines. Based on the literaturesurvey and a full study of the concept and technical architecture of the EHM, considering theproblems in the practice, the main topics of the thesis focus on the gas path components conditionmonitoring, health status assessment as well as remaing useful life prognostics methods. On one sideit can provide technologies and methods for the improvement of the civil aircraft engine EHMcapability, on the other side it can provide experiences and mature technologies for the domesticallyproduced engine’s EHM system. The main topics of the thesis are as follows:
(1) Gas path components condition monitoring:
In order to detect the anomaly as soon as possible from the delta parameter parameters with largenoise and scatters, which maybe cover the fault signature, then to trigger a timely warning at early stageof the fault, a Bayesian factor-based method is proposed to monitor and analyze parameter delta series.Since the “generic” baseline model embedded in the OEM software cannot capture the characteristicsof the individual engine under the real operating conditions, the Multi State Estimation Technique(MSET) is proposed to build the individual baseline model for the specific engine, which can capturethe characteristics of the individual engine. Based on the individual baseline model, more accurate deltadata can be obtained which can advance the fault warning in time. In the application of the new gaspath condition monitoring techniques, the Exhaust Gas Electrostatic Monitoring Signal (EGEMS) isthought as a new gas path performance parameter, and two EGEMS baseline model building methodsare proposed. One is based on one parameter-the fuel flow rate, and the other one is based on multiparameters which are correlated using the MSET. Based on the developed baseline model, the deltabetween the real RMS value of the EGEMS and baseline value is monitored in real time to monitor thegas path component condition and to trigger a warning once some fault occures.
(2) Gas path components health assessment:
A study of in-field engine components health assessment techniques based on adaptive engineperformance model is carried out. Considering the fact that less measured performance parameters thanthe health parameters to estimate and the influence of the measurement noise, the health parametersestimation problem is changed into an optimization problem. An exclusive method based faultdetection and assessment framework is proposed, in which the healthy module is excluded one-by-one to reduce the number of the health parameters to estimate until they are less than the measuredparameters. Considering the fact that the gradual performance deterioration in field is inevitable, ahealth parameter estimation framework is proposed to incorporate the gradual performancedeterioration information. In this framework, the gradual performance deterioration is tracked to get thedegradation state of each module before the fault, then the information is incorporated when isolatingand assessment the fault to improve the health assessment results. A information fusion based gas pathanalysis framework is proposed to tackle the issue of lack of enough measured gas path parameters. Inthis framework, an information fusion mechanism based on the Bayesian network is developed toincorporate the the diagnosis information from multi sources, in which the qualitative information isincorporated by the fault mode prior probability table and the quantitative information is incorporatedby the prior distribution of the health parameter, then the information is fused using Bayesian rules toimprove the accuracy and precision of the estimation results of the health parameters.
(3) Modeling methods for remaining useful life prognostics:
The methods on remaining useful life prognosis for individual system under real operatingconditions are discussed in depth. The state space-based degradation model combined with Bayesianstate estimation theory is proposed for system remaining useful life prognostics and in-servicereliability estimation. The EGTM parameter is used as a degradation parameter to quantify thedegradation state of the engine. Then linear Gaussian state space model is adapted to describe thedegradation trajectory based on the observed EGTM data, and then the conjugate Bayesian inferenceis carried out to estimate the degradation state and further to make a prediction of the failure time. Thestate space based degradation model differentiates the noisy observation from the true degradationstate, which is closer to the actual case. The state space degradation model does not need to makestationarity assumption, so it can effectively manage the situation when there is a sudden change inthe health state due to fault or maintenance. Considering the problems that a single parameter cannotcharacterize the health state of a complex system, a fusion mechanism is developed to fuse multiparameters to get a health index to characterize the health state of the gas path component, based onwhich a state space degradation model is established to describe the degradation path and predict theremaining useful life. For the crack growth failure of the critical components, a fusion framework isproposed to integrate the damage monitoring data and physics of failure mechanism for remaininguseful life prediction. A state space based crack growth model is developed based the Paris crackgrowth model, then the damage monitoring data is integrated using the Bayesian rule. By integratingthe monitoring data the prognostics uncertainty can be continued reduced.
引文
[1] Olsen K., Commercial Aviation Safety Team (CAST) Update,2006,2.
[2] Ashok N. S., Robert W. M., Claudia M. Integrated Vehicle Health Management, Technical Plan,Version2.03.2009. National Aeronautics and Space Administration Aeronautics Research MissionDirectorate Aviation Safety Program.
[3] Asiedu Y., Gu P., Product life cycle cost analysis: state of the art review, Int.J.Prod.Res.,1998,36(N4),883–908.
[4] Bird G.., Christensen M., Lutz D., Scandura P. A., Use of integrated vehicle health management inthe field of commercial aviation, Proceedings of the1st International Forum on System HealthEngineering and Management in Aerospace–NASA ISHEM Forum2005,Napa,California,USA,7–102005, paperno.12.
[5] Alford L. D., The problem with aviation COTS [J]. Aerospace and Electronic Systems Magazine,IEEE,2001,16(2):33-37.
[6] Mercer C. R., Simon D. L., Hunter G. W. et al. Fundamental Technology Development for Gas-turbine Engine Health Management [R]. NASA-TM-2007-00223642007.
[7] Michael G. P., Prognostics and Health Management of Electronics. John Wiley&Sons.,2008.
[8] Vachtsevanos G., Lewis F. L., Roemer M., A. Hess, B. Wu. Intelligent Fault Diagnosis and Prognosisfor Engineering Systems. John Wiley&Sons,2006.
[9] Stephen B. J. et al., System Health Management: with Aerospace Applications
[10] Fox J., Glass B. J., Impact of integrated vehicle health management (IVHM) technologies on groundoperations for reusable launch vehicles (RLVs) and spacecrafts. In Proceedings of the2000IEEEAerospace Conference, Big Sky, Montana, USA,18–25March2000, vol.2, pp.179–186.
[11] Williams Z., Benefits of IVHM: an analytical approach. In Proceedings of the2006IEEE AerospaceConference, Big Sky, Montana, USA,4–11March2006, paper no.1507.
[12] Byer B., Hess A., Fila L. Writing a convincing cost benefit analysis to substantiate autonomiclogistics. In Proceedings of the2001IEEE Aerospace Conference, Big Sky, Montana, USA,10–17March2001, vol.6, pp.3095–3103.
[13] Hess, A., Fila L. Prognostics from the need to reality–from the fleet users and PHM systemdesigner/developers perspectives. In Proceedings of the2002IEEE Aerospace Conference, Big Sky,Montana, USA,9–16March2002, vol.6, pp.2791–2797.
[14] Benedettini O., Baines T. S., Lightfoot H. W., Greenough R. M., State-of-the-art in integratedvehicle health management. Journal of Aerospace Engineering,223(2), pp.157-170.
[15] Cheng S. F., Michael H. Azarian, Michael G. Pecht. Sensor Systems for Prognostics and HealthManagement. Sensors2010,10,5774-5797.
[16] Prosser W. H.; Brown T. L.; Woodard S. E.; Fleming, G. A, et al., Sensor Technology for IntegratedVehicle Health Management of Aerospace Vehicles. REVIEW OF PROGRESS INQUANTITATIVE NONDESTRUCTIVE EVALUATION:Volume22. AIP Conference Proceedings,Volume657, pp.1582-1589,2003.
[17] Nezih M., State of Development of Advanced Sensory Systems for Structural Health MonitoringApplications, Proceedings of the NATO RTO AVT-144Workshop on Enhanced Aircraft PlatformAvailability Through Advanced Maintenance Concepts and Technologies, Vilnius, Lithuania,3-5October2006(DRDC Atlantic SL-2008-260).
[18] Meredith K., Safai M., Georgeson G., New MEMS technologies for integrated vehicle healthmanagement and fluid sensing applications. IEEE Aerospace conference,2009.
[19] SAE E-32Aerospace Propulsion Systems Health Management Committee. Prognostics for GasTurbine Engines. SAE Standard AIR5871,2008.
[20] Papazian J. M., Anagnostou E. L., Engel S. J. A. et al., structural integrity prognosis system.Engineering Fracture Mechanics76(2009)620-632.
[21] Roemer M. J., Dzakowic J., Orsagh R.F., Validation and Verification of Prognostic and HealthMangement Technologies. IEEE Aerospace conference,2004.
[22] Reed E., Schumann J., Mengshoel O. J., Verification and Validation of System Health ManagementModels Models using Parametric Testing. AIAA-2011-1445,2011.
[23] Saxena A., Celaya J., Balaban E., Saha B., Saha S., Goebel K., Metrics for evaluating performanceof prognostic techniques, International Conference on Prognostics and Health Management(PHM08),2008, Denver CO, pp.1-17.
[24] Hess A., Prognostics and Health Management-A Thirty-Year Retrospective, Joint Strike FighterProgram Office, http://ti.arc.nasa.gov/projects/ishem/papers_pres.php.
[25] Hess A., The Prognostic Requirement for Advanced Sensors and Non-Traditional DetectionTechnologies. DARPA/DSO Prognosis Bidder’s Conference September26-27,2002, Alexandria,VA.
[26]王克昌.液体火箭发动机的健康管理系统[J].上海航天,1992(1):27-35.
[27]郑哲敏,赵亚溥. PHM——机械失效的预测和安全管理系统[J].力学进展,1999,29(2):268,243.
[28]孙博,康锐,谢劲松.故障预测与健康管理系统研究和应用现状综述[J].系统工程与电子技术,2007,29(10):1762-1767.
[29]张宝珍.预测与健康管理技术的发展及应用[J].测控技术,2008,27(2):5-7.
[30]常琦,袁慎芳.飞行器综合健康管理(IVHM)系统技术现状及发展[J].系统工程与电子技术,2009,31(11):2652-2657.
[31]马宁,吕琛.飞机故障预测与健康管理框架研究[J].华中科技大学学报（自然科学版）,2009,37(1):207-209（增刊）.
[32]李文娟,马存宝,贺尔铭.综合飞行器健康管理系统组成框架及关键技术研究[J].航空工程进展,2011,2(3):330-334.
[33] Urban L.A., Gas Path Analysis Applied to Turbine Engine Conditioning Monitoring. AIAA/SAEPaper1972,72-1082.
[34] Urban L.A. Parameter selection for multiple fault diagnostics of gas turbine engines. AGARDConference Proceedings, No.165,1974, Zurich, Switzerland.
[35] Wade R. A., A Need-focused Approach to Air Force Engine Health Management Research,Proceedings of the2005IEEE Aerospace Conference, Big Sky, Montana, March2005.
[36] Jaw L. C., Recent Advancements in Aircraft Engine Health Management (EHM) Technologies andRecommendations for the Next Step. ASME Turbo Expo2005: Power for Land, Sea and Air, June,2005.
[37] SAE. E-32Committee Fact Sheet: SAE Technical Committee E-32Aerospace Propulsion SystemsHealth Management, http://www.sae.org/servlets/works/committeeHome.do?comtID=TEAE32.
[38] SAE. Aircraft Gas Turbine Engine Health Management System Guide, SAE ARP1587, Revison B,2007.
[39] Fisher C. E., Data and information fusion for gas path debris monitoring, IEEE AerospaceConference Proceedings,2001.
[40] Powrie H. E. G., Novis A., Gas path debris monitoring for F-35Joint Strike Fighter propulsionsystem PHM, IEEE Aerospace Conference Proceedings,2006.
[41] Tappert P., von Flotow A., Mercadal M., Autonomous PHM with blade-tip-sensors: algorithms andseeded fault experience, Aerospace Conference,2001, IEEE Proceedings,2001.
[42] von Flotow A., Mercadal M., Tappert P., Health monitoring and prognostics of blades and disks withblade tip sensors.2000IEEE Aerospace Conference Proceedings, Big Sky, MT, USA.
[43] Deol D. L., TEMPER—A gas-path analysis tool for commercial jet engines. J. Eng. Gas TurbinesPower, Trans ASME,1994,116,82–89.
[44]范作民孙春林白杰.航空发动机故障诊断导论[M].北京科学出版社2004.
[45] Stamatis A., Mathioudakis K., Smith M., Papailiou K., Gas turbine component fault identification bymeans of adaptive performance modeling. ASME Paper90-GT-376,1990.
[46] Doel D. L., Interpretation of Weighted-Least-Squares Gas Path Analysis Results, Transactions of theASME,2005, Vol.125:624-633.
[47] Volponi A. J., Gas path analysis: An approach to engine diagnostics.35th Symposium MechanicalFailures Prevention Group, Gaithersbury, MD, April1982.
[48] Provost M. J. COMPASS: A generalized ground-based monitoring system. AGARD-CP-449, EngineCondition Monitoring—Technology and Experience, October1988.
[49] Urban L. A., Volponi A. J., Mathematical methods of relative engine performance diagnostics, SAE1992Transactions Journal of Aerospace, Section1, Vol.101, SAE Technical Paper No.922048,1992.
[50] Dewallef P., Leonard O., Mathioudakis K., On-line aircraft engine diagnostic using asoft-constrained kalman filter [C]. ASME Paper No.GT2004-53539,2004.
[51] Aretakis N., Mathioudakis K., Stamatis A., Non-Linear Engine Component Fault Diagnosis From aLimited Number of Measurements Using a Combinatorial Approach, ASME J. Eng. Gas TurbinesPower,2003,125, pp.642–650.
[52] Mathioudakis K., Kamboukos Ph., Stamatis A., Gas turbine component fault detection from alimited number of measurements [C]. Proc. Inst. Mech. Eng., Part A,2004,218:609–618.
[53] Torella G.., Lombardo G.., Neural Networks for the diagnostics of gas turbine engines. ASME96-TA-39, ASME Turbo Asia Conference,1996.
[54] Romessis A., Stamatis A., Mathioudakis K., A parametric investigation of the diagnostic ability ofprobabilistic neural networks on turbofan engines. ASME2001-GT-0011, ASME Turbo Expo2001,New Orleans, Louisiana, June2001.
[55] Dong-Hyuck Seo, Tae-Seong Roh, Dong-Whan Choi. Defect diagnostics of gas turbine engine usinghybrid SVM-ANN with module system in off-design condition. JOURNAL OF MECHANICALSCIENCE AND TECHNOLOGY,2009, Volume23, Number3,677-685.
[56]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,2005, Vol.26（6）：686-690.
[57] Tang G.., Yates C. L., Zhang, J., Chen D., A practical intelligent system for condition monitoring andfault diagnosis of jet engines. AIAA99-2533,1999.
[58] Ganguli R., Application of fuzzy logic for fault isolation of jet engines. ASME2001-GT-0013,ASME Turbo Expo2001, New Orleans, Louisiana, June2001.
[59] Vivian, B., Singh, R., Application of expert system technology to gas path analysis of a single shaftturboprop engine.5th European Propulsion Forum, Pisa, Italy,5–7April1995.
[60] Zhernakov S. V., Diagnostics and Checking of Gas-Turbine Engines Parameters with Hybrid ExpertSystems, Proceedings of the Workshop on Computer Science and Information Technologies,2000.
[61] Jaw L., Neural Networks for Model-based Prognostics, Proceedings of IEEE Aerospace Conference,Aspen, Colorado, March1999.
[62] Volponi A., Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management, NASA/CR-2005-214055, E-15412,2005.
[63] Harvey T. J., Wood R. J. K., Powrie H. E. G., Electrostatic wear monitoring of rolling elementbearings. Wear,2007,263:1492-1501.
[64] Barragan J. A. M., Engine Vibration Monitoring and Diagnosis Based on On-board Captured Data,NATO AVT Symposium on Aging Mechanism and Control, Manchester, UK,2001.
[65] Orsagh R. F., Sheldon J., Klenke C. J.; Prognostics/Dignostics for Gas Turbine Engine Bearings,Proceedings of IEEE Aerospace Conference, Big Sky, Montana, March2003.
[66] Simon D., et al., Sensor Needs for Control and Health Management of Intelligent Aircraft Engines,Proceedings of ASME Turbo Expo2004; GT2004-54324.
[67] Hudak S. J., et al., A Probabilistic Analysis of the Benefits of In-Service Fatigue DamageMonitoring for Turbine Engine Prognosis.45th AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics&Materials Conference, April2004.
[68] Hudak S. J., Enright P. M., McClung C. R., et al. Enhanced Life Prediction Technology for EngineRotor Life Extension (ERLE). AFRL-RX-WP-TR-2008-4287,2008.
[69] Holtz C., Smith G., Friend R., Modernizing systems through data integration-A vision for EHM inthe United States Air Force [A]. Proceeding of AIAA/ASME/SAE/ASEE40th Joint PropulsionConference and Exhibit [C]. Fort Lauderdale, FL. AIAA, Inc,2004:1-12.
[70] www.wpafb.af.mil/news/story_print.asp?id=123221807
[71] Clark G. J., Vian J. L., West M. E., et al., Multi-platform Airplane Health Management [C].IEEEAerospace onference.2007:1-13.
[72]费成巍,艾延廷.航空发动机健康管理系统设计技术[J].航空发动机,2009,35(5):24-29
[73]姜彩虹,孙志岩,王曦.航空发动机预测健康管理系统设计的关键技术[J].航空动力学报,2009,24(11):2589-2594.
[74]王施,王荣桥,陈志英,樊江,申秀丽.航空发动机健康管理综述[J].燃气涡轮试验与研究,2009,22(1):51-58.
[75]文振华,左洪福,李耀华.气路颗粒静电监测技术及实验[J].航空动力学报,2008,23(12):2321-2326.
[76]李耀华,左洪福,刘鹏鹏.某型航空涡轮轴发动机尾气静电监测探索性实验[J].航空学报,2010,31(11):2174-2181.
[77]孙见忠,左洪福,詹志娟,刘鹏鹏.涡轴发动机尾气静电信号影响因素分析[J].航空学报,2012,32(3):412-420.
[78]陈志雄,左洪福,詹志娟,张营.滑油系统全流量在线磨粒静电监测技术研究[J].航空学报,2012,32(3):437-443.
[79]段发阶,方志强,孙宇扬,叶声华.叶尖定时旋转叶片实时振动测量技术.光电工程,2005,Vol.32, No.328-31.
[80]段发阶,张玉贵,等.航空发动机旋转叶片振动监测系统研究.光学与光电技术,2008, Vol.6,No.148-51.
[81]陈卫,程礼,李全通,高卫星,航空发动机监控技术[M].北京：国防工业出版社,2011.
[82]李长征雷勇.航空发动机气路故障诊断[J].测控技术200625(8):21-24.
[83]蒋亮,李书明,郝英,等.航空发动机气路故障诊断研究现状[J].中国民航飞行学院学报200523(增刊)60-65.
[84]陈恬,孙健国,郝英.基于神经网络和证据融合理论的航空发动机气路故障诊断[J].航空学报200627(6):1014-1017.
[85]张鹏,黄金泉.航空发动机气路故障诊断的平方根UKF方法研究[J].航空动力学报,2008.
[86] Chen G., Yang Y. W., Zuo H. F., Intelligent Fusion for Aero engine Wear Fault Diagnosis,Transactions of Nanjing University of Aeronautics&Astronautics,2006,23(4):297-303.
[87]陈恬,孙健国,郝英,基于神经网络和证据融合理论的航空发动机气路故障诊断,航空学报,2006,27（6）：1014-1017.
[88]杨建平,黄洪钟,苗强等,基于证据理论的航空发动机早期故障诊断方法,航空动力学报,2008,23（12）：2327-2331.
[89]曲建岭,唐昌盛,肖辉雄等,人工神经网络融合诊断航空发动机气路故障,航空动力学报,2008,23（11）：2124-2127.
[90]鲁峰,黄金泉,陈煜,航空发动机部件性能融合故障诊断方法研究,航空动力学报,2009年第7期.
[91]黄伟斌.发动机健康管理的自适应机载实时模型[D].[硕士论文],南京:南京航空航天大学,2007.
[92]袁春飞,姚华,杨刚,航空发动机机载实时自适应模型研究,航空学报,2006,27（4）：561-564.
[93]陈果,用结构自适应神经网络预测航空发动机性能趋势[J].航空学报2007,28(3)535-539.
[94]胡金海,谢寿生,骆广琦等,基于支持向量机方法的发动机性能趋势预测[J].推进技术200526(3)260264.
[95]任淑红,左洪福.基于性能衰退的航空发动机剩余寿命组合预测方法[J].机械科学与技术,2011,30(1):23-29.
[96]吕永乐,郎荣令,路辉等,航空发动机性能参数联合RBFPN和FAR预测[J].北京航空航天大学学报,2010,36(2):131-134.
[97]张宝诚,刘孝安.航空发动机可靠性和经济性[M].北京:国防工业出版社,1998.
[98]王通北,陈美英.军用航空发动机的可靠性和寿命[J].航空发动机,1994(1):36-47.
[99]洪杰,张大钧,韩继斌.航空发动机关键件使用寿命监视系统设计[J].北京航空航天大学学报,2000,26(1):45-48.
[100]甘晓华,李伟.现役航空发动机使用寿命确定和控制方法[J].航空工程进展,2010,1(2):103-106.
[101] Litt J. S., Simon D. L., A Survey of Intelligent Control and Health Management Technologies forAircraft Propulsion Systems. JOURNAL OF AEROSPACE COMPUTING, INFORMATION, ANDCOMMUNICATION, Vol.1, pp.543-563,2004.
[102] Jaw L., Mattingly J., Aircraft engine controls: design, system analysis, and health monitoring. AIAA,2009.
[103] Tu F., Ghoshal S., Luo J., et al., PHM Integration with Maintenance and Inventory ManagementSystems. IEEE Aerospace Conference.2007.
[104] Camci F., Valentine G. S., Navarra K., Methodologies for Integration of PHM Systems withMaintenance Data. IEEE Aerospace Conference.2006.
[105]钟诗圣,栾圣罡.面向航空发动机全寿命周期管理的航线数据处理系统[J].计算机集成制造系统,2006,12(8):1273-1278.
[106]戎翔.民航发动机健康管理中的寿命预测与维修决策方法研究[D].[博士论文],南京：南京航空航天大学,2008.
[107]姚捷.浅谈CFM56-5B4航空发动机状态监控[J].江苏航空,2011,123(3):35-36.
[108]涂杰,苏有生,张雷.2007年度国内运输航空器发动机使用和空中停车统计分析[J].中国民用航空,2008,88:54-56.
[109]黄郁华.发动机空中停车的故障分析与预防措施[J].民航经济与技术，1996,173:35-37.
[110]李丹,段宝君.不同寿命控制方式下单元体发动机大修经济性分析[J].航空维修,2004,2:45-46.
[111]于晓伟,邢晨光,王萍,张宝珍. F119发动机的维修保障模式, http://home.cetin.net.cn/qrms/file/201001/upFiles/2f7edd82-bc2e-4853-9878-882862f9da84.pdf.
[112]张萍,航空发动机单元体设计的维护实践[J].民用飞机设计与研究,37-40.
[113]成国伟,毛红军,任淑红.民用航空发动机外包维修成本的控制[J].江苏航空,2008,3:20-22.
[114]梁剑,左洪福,常继百,等.基于统计粗集模型的航空发动机维修等级预测方法研究[J].应用科学学报,2005,23(2):196-199.
[115]张海军,左洪福,梁剑.基于信息熵属性约简的航空发动机送修等级决策[J].系统工程,2005,23(7):105-108.
[116]付旭云,钟诗胜,丁刚.民用航空发动机单元体送修工作范围决策[J].航空动力学报,2010,25(10):2195-2200.
[117]徐可君,江龙平.军用航空发动机可靠性和寿命管理[J].中国工程科学,2003,5(1):82-88.
[118] GEAE/CFMI, GE Aircraft Engines Monitoring Training Course Material.
[119] GE, DiagnosticsTrend Interpretation Training Material,2007.
[120] West M., Harrison P. J., Bayesian forecasting and dynamic models [M].2nd Edn, Springer-Verlag,New York,1997.
[121] Petris G., Petrone S., Campagnoli P., Dynamic Linear Models with R [M]. Springer-Verlag, NewYork,2009.
[122]林兆福,范作民.发动机基线方程的建立和应用.中国民航学院学报.1992,12,10(4)：20-32.
[123]钟诗胜,崔智全,付旭.Rolls-Roycs发动机基线挖掘方法.计算机集成制造系统.2010.10.16(10)：2265-2270.
[124]钟诗胜,周志波,张永,康力平.基于三次回归分析的试车台基线库的建立[J].计算机集成制造系统,2005,11(2).
[125] TEACHES: Turbine engine advance calculation and health assessment educational software(turbofan engine version), National Technical university of Athens.
[126] Dewallef P., Application of the Kalman filter to health monitoring of gas turbine engines: asequential approach to robust diagnosis. PhD Thesis, University of Liège,2005.
[127] The R Project for Statistical Computing. http://www.r-project.org/.
[128] Isermann R., Balle P., Trends in the Application of Model-Based Fault Detection and Diagnosis ofTechnical Processes,Control Eng. Practice, vol.5, no.5,1997, pp.709-719.
[129] Hines J. W., Garvey D., Seibert R., Usynin A., Technical review of on-line monitoring techniquesfor performance assessment, Volume2: Theoretical issues. U.S. Nuclear Regulatory Commission,Washington, DC, NUREG/CR-6895,2007
[130] SINGER R.M., GROSS K.C., HERZOG J.P., KING R.W., WAGERICH S.W., Model-BasedNuclear Power Plant Monitoring and Fault Detection: Theoretical Foundations, Proc.9th Intl. Conf.on Intelligent Systems Applications to Power Systems, Seoul, Korea,(1996).
[131] GRIBOK A.V., Hines J.W., Uhrig R.E., Use of Kernal Based Techniques for Sensor Validation inNuclear Power Plants, The Third American Nuclear Society International Topical Meeting onNuclear Plant Instrumentation and Control and Human-Machine Interface Technologies, WashingtonDC, November13-17,2000.
[132] GRIBOK A.V., Hines J.W., Urmanov A.M., Uncertainty Analysis of Memory Based SensorValidation Techniques, Real Time Systems,2004,27(1),7-26.
[133] Bickford R. L., et al., MSET Signal Validation System for Space Shuttle Main Engine, Final Report,NASA Contract NAS8-98027,2000,8.
[134] Bickford R. L., Meyer C., Lee V., Online Signal Validation for Assured Data Quality, Proc. of the2001Instrument Society of America,2001.
[135] Gross K. C., Lu W., Early Detection of Signal and Process Anomalies in Enterprise ComputingSystems, Proc.2002IEEE Int’l Conf. on Machine Learning and Applications (ICMLA), Las Vegas,NV, June2002.
[136] Wegerich S., Similarity Based Modeling of Time Synchronous Averaged Vibration Signals forMachinery Health Monitoring, Proceedings, IEEE Aerospace Conference, Big Sky, MT, USA,March6-13,2004.
[137] Wegerich S., Similarity Based Modeling of Vibration Features for Fault Detection and Identification,Sensor Review, Vol.25, Issue2,2005.
[138]姚良,李艾华,孙红辉,张振仁.基于MSET和SPRT的内燃机气阀机构振动监测[J].振动工程学报,2009,22(2):150-155.
[139] Black L. C., Uhrig R. E, Wesldy H. J., System modeling and instrument calibration verification witha nonlinear state estimation technique[A]. Proceedings of the Maintenance and ReliabilityConference[C]. Knoxville, TN, May,1998.
[140] Cherkassky V., Mulier F., Learning From Data, John Wiley&Sons,1998.
[141] Hines J. W., Garvey D., Traditional and Robust Vector Selection Methods for Use with SimilarityBased Models,5thInternational Topical Meeting on Nuclear Plant Instrumentation, Control andHuman-Machine Interface Technologies, Albuquerque, NM,2006.
[142] Hines, J. W., Usynin A., MSET Performance Optimization Through Regularization, NuclearEngineering and Technology,38(2)(April2005).
[143]葛忠汉.飞机发动机空停事件及预防[J].航空维修工程,2009,1:46-48.
[144] Couch R. P., Rossback D. R., Burgess R. W., et al., Sensing incipient engine failure withelectrostatic probes [C]//Instrumentation for Airbreathing Propulsion, Proceeding of theSymposium.1972:519-529.
[145] Couch R. P., Detecting abnormal turbine engine deterioration using electrostatic methods, Journal ofAircraft,1978,15(10):692-695.
[146] Fisher C. E., Gas path condition monitoring using electrostatic techniques, Engine ConditionMonitoring: Technology and Experience,1988.
[147] Powrie H. E. G., Mcnicholas K., Gas path condition monitoring during accelerated mission testingof a demonstrator engine, AIAA-1997-2904,1997.
[148] Powrie H. E. G., Fisher C. E., Engine health monitoring: Towards total prognostics, IEEE AerospaceConference Proceedings,1999.
[149] Fisher C. E., Gas path debris monitoring-a21st century PHM tool, IEEE Aerospace ConferenceProceedings,2000.
[150] Fisher C. E., Gas turbine condition monitoring systems—an integrated approach, IEEE AerospaceConference Proceedings,2000.
[151]文振华,左洪福,王华等.航空发动机气路静电监测传感器特性[J].传感器与微系统,2008,27(11):28-31.
[152]李耀华,左洪福.碰摩故障静电监测方法及模拟实验[J].航空学报,2010,31(6):1156-1163.
[153]金如山.航空燃气轮机燃烧室[M].北京:宇航出版社,1988.
[154] Frenklach M., Clary D. W., Gardiner Jr W. C., Stein S. E., Detailed kinetic modeling of sootformation in shock-tube paralysis of acetylene. Process to Combustion Institute,20:887-901,1985.
[155] Bansal G., Mueller M. E., Pitsch H., Direct numerical simulation of soot formation in jet-enginecombustors. Center for Turbulence Research Annual Research Briefs2009.
[156]李红红.航空发动机二维模型燃烧室中碳黑颗粒生成数值模拟.南京航空航天大学,硕士论文,2008.
[157] Lefebvre A H., Gas turbine combustion, Washington, Hemisphere Pub. Corp.,1983.
[158] SAE. Aircraft Gas Turbine Exhaust Measurements, SAE ARP1179, Revision B, SAE, Warrendale,PA.1991.
[159] Wilson C. W., Petzold A., Nyeki S., Schumann U., Zellner R., Measurement and Prediction ofEmissions of Aerosols and Gaseous Precursors from Gas Turbine Engines (PartEmis): An Overview.Aerospace Science and Technology, Vol.8, No.2,2004, pp.131–143.
[160] Petzold A., et al., Particle emissions from aircraft engines–a survey of the European projectPartEmis. Meteorologische Zeitschrift,2005,14(4),465-476.
[161] Wey C.C., Anderson B.E., et al., Aircraft Particle Emissions Experiment (APEX). NASA/TM-2006-214382, ARL-TR-3903. U.S. National Aeronautics and Space Administration, Glenn ResearchCenter, Cleveland, OH. Available at: http://gltrs.grc.nasa.gov,2006.
[162] Kinsey J. S., Characterization of Emissions from Commercial Aircraft Engines During the AircraftParticle Emissions Experiment (APEX)1to3. EPA-600/R-091/130. U.S. Environmental ProtectionAgency, Research Triangle Park, NC,2009.
[163] Kinsey J., Dong Y., Williams D. C., Logan R., Physical characterization of the fine particleemissions from commercial aircraft engines during the Aircraft Particle Emissions eXperiment(APEX)1-3. Atmospheric Environment2010,44:2147-2156.
[164] Kinsey J., Hays M., Dong Y., Williams D., Logan R., Logan, Chemical Characterization of the FineParticle Emissions from Commercial Aircraft Engines during the Aircraft Particle EmissionseXperiment (APEX)1to3. Environ Science Technology.2011,45(8):3415-21.
[165] Onasch T. B., Jayne J. T., Herndon S., Worsnop D. R., MiakeLye R. C., Mortimer I. P., Anderson B.E., Chemical properties of aircraft engine particulate exhaust emissions, Journal of Propulsion andPower,25,1121–1137.
[166] Timko M. T., Onasch T. B., Northway M. J., Jayne J. T., Canagaratna M. R., Herndon S. C., Wood E.C., Miake-Lye R. C., Knighton W. B.. Gas turbine engine emissions Part II: Chemical properties ofparticulate matter [J]. J. Eng. Gas Turb. Power,2010,132,061505(15pages).
[167] Whitefield P. D., et al., Summarizing and Interpreting Aircraft Gaseous and Particulate EmissionsData. Airport Cooperative Research Program Report9, Transportation Research Board,2008.
[168] Dopelheuer A., Lecht M., Influence of Engine performance on Emissions Characteristics [C].Symposium of the applied vehicle Technology Pane-Gas Turbine Engine Combustion, Emissionsand alternative fuels, Lisbon, Portugal,1998.
[169]陈锐,周彤,顾铭企.某型发动机主燃烧室积碳的排除[J].航空发动机,1996(3).
[170]姚四伟,张力先,霍岩.发动机燃烧室部件故障的分析与预防[J].西安航空技术高等专科学校学报,2008,26(1):18-20.
[171] Chupp R. E., Ghasripoor F., Moore G. D., et al., Applying Abradable Seals to Industrial GasTurbines,2002GRC193,2002.
[172] Kennedy Jr F. E., Thermomechanical effects in high-speed seal rubs, NASA-CR-180418,1987.
[173] Chappel D., Howe H., Vo L., Abradable Seal Testing: Blade Temperatures During Low Speed RubEvent,2001-3479,2001.
[174] Wang H., Criteria for analysis of abradable coatings, Surface&coatings technology,1996,79(1-3):71-75.
[175]宋兆泓,陈光,张景武,洪其麟.航空发动机典型故障分析[M].北京航空航天大学出版社,1993.
[176] Di Domenico M., Gerlinger P., Aigner M.. Development and validation of a new soot formationmodel for gas turbine combustor simulations. Combustion and Flame,2010,157:246–258.
[177] Bansal G., Mueller M. E., Pitsch H., Direct numerical simulation of soot formation in jet-enginecombustors. Center for Turbulence Research Annual Research Briefs2009.
[178]李红红.航空发动机二维模型燃烧室中碳黑颗粒生成数值模拟.南京航空航天大学,硕士论文,2008.
[179]柴志刚.某涡轴发动机Ⅰ级涡轮叶片叶尖涂层剥落失效分析[J].失效分析与预防,2007,2(1):
[180]梁剑,左洪福,常继百,赵红华,周左成,基于统计粗集模型的航空发动机维修等级预测方法研究.应用科学学报,2005,23(2):196~199
[181]付旭云,钟诗胜,丁刚,民用航空发动机单元体送修工作范围决策[J].航空动力学报,2010(10):2195‐2200.
[182]徐可君,基于单元体的军用航空发动机寿命控制和管理.海军航空工程学院学报,2007,22(5):541-547.
[183] Turevskiy A., Meisner R., Luppold R., Kern R., Fuller J., A Model Based Controller for CommercialAero Gas Turbines, ASME paper GT-2002-30041,2002.
[184] Lietzau K., Kreiner A., Model Based Control Concepts for Jet Engines, ASME paper2001-GT-0016,2001.
[185] Kamboukos Ph., Mathioudakis K., Comparison of linear and non-linear gas turbine performancediagnostics. Journal of Engineering for Gas Turbines and Power,2005, Vol.127,49-56.
[186] Gulati A, Taylor D., Singh R., Multiple operating point analysis using genetic algorithmoptimization for gas turbine diagnostics [C]. XV ISABE, Bangalore, India, Sept.3–7, Paper No.ISABE2001-1139,2001.
[187] Kamboukos P., Mathioudakis K., Multipoint Non-linear Method for Enhanced Component andSensor Malfunction Diagnosis, ASME Paper No. GT-2006-90451,2006.
[188] Luppold R.H., Gallops G.W., Kerr L.J., Roman J.R. et al., Estimating In-Flight Engine PerformanceVariations Using Kalman Filter Concepts, AIAA-89-2584,1989.
[189] Kerr L. J., Nemec T. S., Gallops G W., Real Time Estimation of Gas Turbine Engine Damage Usinga Control Based Kalman Filter Algorithm, ASME91-GT-216,1991.
[190] Tsalavoutas A., Pothos S., Mathioudakis K., Stamatis A., Monitoring of the Performance of a TwinSpool Ship Propulsion Turbine by Means of Adaptive Modeling, RTO Symposium on Gas TurbineOperation and Technology for Land, Sea and Air Propulsion and Power Systems, Ottawa, Canada,October18–21, Paper No. RTO-MP-34,1999.
[191] Lambiris B., Mathioudakis K., Stamatis A., Papailiou K. D., Adaptive Modeling of Jet EnginePerformance With Application to Condition Monitoring, J. Propul. Power,10(6), pp.890–896,1994.
[192] Lee Y., Singh R., Health Monitoring of Turbine Engine Gas Path Components and MeasurementInstruments, ASME Paper No.96-GT-242,1996.
[193]张鹏,黄金泉,基于双重卡尔曼滤波器的发动机故障诊断,航空动力学报,2008年第5期.
[194] Takahisa Kobayashi, Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank ofKalman Filters, NASA/CR-2003-212298.
[195] Depold H. R., Gass F. D., The application of expert systems and neural networks to gas turbineprognostics and diagnostics. ASME Paper98-GT-101. J. Eng. Gas Turbines and Power, TransASME,1999,121,607–612.
[196] Ganguli R., Data rectification and detection of trend shifts in jet engine gas path measurementsusing median filters and fuzzy logic. ASME2001-GT-0014, ASME TURBO EXPO2001, NewOrleans, Louisiana, June2001.
[197] Zedda M., Singh R., Gas turbine engine and sensor fault diagnosis using optimisationtechniques.AIAA-99-2530,1999.
[198] Nielsen H. B., Immoptibox: A MATLAB TOOLBOX FOR OPTIMIZATION AND DATA FITTING.www2.imm.dtu.dk/~hbn/immoptibox/.
[199] Mathioudakis K., Kamboukos P., Assessment of the Effectiveness of Gas Path Diagnostic Schemes,ASME J. Eng. Gas Turbines Power,128(1), pp.57–63,2006.
[200] Olsson W. J., Stromberg W. J., Aircraft Engine Diagnostics, NASA Lewis Res. Center, pp.43–61,Jan.1981, Contract No. NASA3-20632,1981.
[201] Fasching W. A., Stricklin, R., CF6Jet Engine Diagnostics Program: Final Report,NASA/CR-165582,1982.
[202] Sasahara O., JT9D engine/module performance deterioration results from back to back testing.Turbomachinery Perform Deterioration,25–32,1986.
[203] Crosby J., Factors J., Relating to deterioration based on Rolls-Royce RB211in service performance.Turbomachinery Perform Deterioration,41–47,1986.
[204] Nilkitsaranont Y., Li P., Gas turbine performance prognostic for condition-based maintenance.Applied Energy862152-2161,2009.
[205] Roemer, M.J., Kacprzynski, G.. J., Schoeller, M. H., Improved diagnostic and prognosticassessments using health management information fusion. AUTOTESTCON Proceedings,2001.IEEE Systems Readiness Technology Conference, Page(s):365–377.
[206] Eklund N. H., Goebel K. F., Using Neural Networks and the Rank Permutation Transformation toDetect Abnormal Conditions in Aircraft Engines, IEEE Mid-Summer Workshop on Soft Computingin Industrial Applications, June2005, IEEE0-7803-8942-5, pp.1-5,2005.
[207] Yan W. Z., Kai G., Sensor Validation and Fusion for Gas Turbine Vibration Monitoring[R]. America:GE Global Research,2003.
[208] Alag S., Agogino A. M., Morjaria M., A methodology for intelligent sensor measurement, validation,fusion, and fault detection for equipment monitoring and diagnostics. Artificial Intelligence forEngineering Design, Analysis and Manufacturing,2001,15,307–320.
[209] Donat W., Choi K., An W., et al, Data Visualization, Data Reduction and Classifier Fusion forIntelligent Fault Detection and Diagnosis in Gas Turbine Engines. ASME Turbo Expo2007: Powerfor Land, Sea, and Air (GT2007) May14–17,2007, Montreal, Canada.
[210] Li C., Lei Y., Fault Diagnosis for an Aircraft Engine Based on Information Fusion.2006IEEEInternational Conference on Mechatronics,2006,7Page:199-202.
[211] Sun J. Z., Zuo H. F., Yang H. B., Pecht M.. Study of Ensemble Learning-Based Fusion Prognostics.2010Prognostics&System Health Management Conference (PHM2010Macau), Jan.2010, page1-7.
[212] Alexander P., Singh R., Gas Turbine Engine Fault Diagnostics Using Fuzzy Concepts, AIAA1stIntelligent Systems Technical Conference, Sept.2004, AIAA2004-6223, pp.1-15.
[213]陈果.航空器检测与诊断技术导论.中国民航出版社,2007.
[214] Kyriazis A., Tsalavoutas A., Mathioudakis K., Gas Turbine Fault Identification by Fusing VibrationTrending and Gas Path Analysis. ASME Turbo Expo2009: Power for Land, Sea, and Air (GT2009)June8–12,2009, Orlando, Florida, USA.
[215] Breese J. S., Horvitz E. J., Peot M. A.., Gay R., Quentin, G.. H., Automated Decision-AnalyticDiagnosis of Thermal Performance in Gas Turbines, ASME Paper No.92-GT-399,1992.
[216] Palmer C. A., Combining Bayesian Belief Networks With Gas Path Analysis for Test CellDiagnostics and Overhaul, ASME Paper No.98-GT-168,1998.
[217] Romessis C., Stamatis A., Mathioudakis K., Setting up a Belief Network for Turbofan DiagnosisWith the Aid of an Engine Performance Model, ISABE Paper2001, No.1032,2001.
[218] Romessis C., Mathioudakis K., Bayesian Network Approach for Gas Path Fault Diagnosis, ASME J.Eng. Gas Turbines Power,128(1), pp.64–72,2006.
[219] Lee Y. K., Mavris D. N., Volovoi V. V., et al. A Fault Diagnosis Method for Industrial Gas TurbinesUsing Bayesian Data Analysis. Journal of Engineering for Gas Turbines and Power,2010, Vol.132.
[220] Lee Y. K., Mavris D. N., Volovoi V. V., M. Yuan. A Bayesian method for calibrating computermodels to test data. Inverse Problems in Science and Engineering,2011,19(3):395-408.
[221] Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. MorganKaufman, San Mateo, CA,1988.
[222] Stephenson T A. An Introduction to Bayesian Network Theory and Usage. IDIAP-RR00,03, Feb,2000.
[223] Nilsson N. Artificial Intelligence: A New synthesis Morgan Kaufmanm Publishers, USA,1998.
[224] Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equations of state calculations by fastcomputing machines[J]. Journal of Chemical Physics,1953,21:1087-1091.
[225] Hastings W K. Monte Carlo sampling methods using Markov chains and their application [J].Biometrika,1970,57:97-109.
[226] Geman S and Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration ofimages[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6:721-741.
[227] Lunn D. J., Thomas A., Best N., Spiegelhalter D., WinBUGS-a Bayesian modelling framework:concepts, structure, and extensibility. Statistics and Computing,10:325—337,2000.
[228] Meeker W Q, Escobar L A. Statistical method for reliability data[M]. John Wiley and Sons,1998.
[229] Stephen B., Johnson, T. G., Seth K., System Health Management: with Aerospace Applications.Wiley,2011.
[230] Pecht M. G., Prognostics and health management of electronics. Wiley-Interscience, New York,2008.
[231] Engine Roter Life Extension. http://www.ml.afrl.af.mil/mll/b-metals.html.
[232] Kothamasu R., Huang S.H., VerDuin W.H., System health monitoring and prognostics-a review ofcurrent paradigms and practices, International Journal of Advanced Manufacturing Technology28,20061012-1024.
[233] Heng A., Zhang S., Tan A.C.C., Mathew J., Rotating machinery prognostics: state of the art,challenges and opportunities, Mechanical Systems and Signal Processing23(2009)724-739.
[234] Ray A., Tangirala S., Stochasticmodeling of fatigue crack dynamics for on-line failure prognostics,IEEE Transactions on Control Systems Technology4(1996)443-451.
[235] Kacprzynski G. J., Sarlashkar A., Roemer M.J., Predicting remaining life by fusing the physics offailure modeling with diagnostics, Journal of Metal56(2004)29-35.
[236] Li C. J., Lee H., Gear fatigue crack prognosis using embedded model, gear dynamic model andfracture mechanics, Mechanical Systems and Signal Processing19(2005)836-846.
[237] Valentin R., Osterman M., Newman B., Remaining life assessment of aging electronics in avioncapplications, Proceedings of the Annual Reliability and Maintainability Symposium (RAMS),2003:313-318.
[238] Ramakrishnan A., Pecht M. G., A Life consumption monitoring methodology for electronic systems,IEEE Transactions on Components and Packaging Technologies26(2003)625-634.
[239] Harrison G.F., Smith M. E. F., hurse J., Procedure for aeroengine component life usage prediction,AIDAA/AAAF/DGLR/RAeS5th European Propulsion Forum, Pisa, Italy,1995.
[240] Yan J., Koc M., Lee J., A prognostic algorithm for machine performance assessment and itsapplication, Production Planning and Control76(2004)796-801.
[241] Swanson D.C., A general prognostic tracking algorithm for predictive maintenance, in: IEEEAerospace Conference (2001)2971-2977.
[242] Orchard M.E., Vachtsevanos G. J., A particle-filtering approach for on-line fault diagnosis andfailure prognosis, Transactions of the Institute of Measurement and Control31(4)(2009)221-24.
[243] Tse P. W., Atherton D. P., Prediction of machine deterioration using vibration based fault trends andrecurrent neural networks, Transactions of the ASME: Journal of Vibration and Acoustics121(3)(1999)355-362.
[244] Wang P., Vachtsevanos G., Fault prognostics using dynamic wavelet neural networks, ArtificialIntelligence for Engineering Design, Analysis and Manufacturing15(2001)349-365.
[245] Yang B.S., Widodo A., Support vector machine for machine fault diagnosis and prognosis, Journalof System Design and Dynamics2(1)(2008)12-23.
[246] Wang W. Q., Golnaragh M. F., Ismail F., Prognosis of machine health condition using neuro-fuzzysystems, Mechanical Systems and Signal Processing18(2004)813-831.
[247] Kharoufeh J. P., Cox S. M.(2005). Stochastic models for degradation-based reliability. IIETransactions,37(6),533-542.
[248] Kharoufeh J. P., Solo C., Ulukus M. Y., Semi-Markov models for degradation-based reliability. IIETransactions42(8)(2010)599-612.
[249] Enright M.P., Hudak S.J., McClung R.C., Probabilistic treatment of aircraft engine usage,GT2008-51393, Proceedings of ASME Turbo Expo2008, Power for Land, Sea, and Air,2008,Berlin, Germany.
[250] Research and Technology Organization of NATO. Recommended practices for monitoring gasturbine engine life consumption, Report of the Applied Vehicle Technology Panel Task GroupAVT-017. RTO Technical Report28(RTO-TR-28, AC/323/(AVT)TP/22),2000.
[251] Mathew S., Das D., Osterman M., Pecht M., Ferebee R., Prognostic Assessment of AluminumSupport Structure on a Printed Circuit Board, ASME Journal of Electronic Packaging, Vol.128,No.4, pp.339-345, December2006.
[252] Shetty V., Das D., Pecht M., Hiemstra D., Martin S., Remaining Life Assessment of Shuttle RemoteManipulator System End Effector, Proceedings of the22nd Space Simulation Conference, EllicottCity, MD, October21-23,2002.
[253] Barker J. Gu, D., Pecht M., Prognostics Implementation of Electronics under Vibration Loading,Microelectronics Reliability, Vol.47, No.12, pp.1849-1856, December2007.
[254] Enright M.P., Hudak S.J. Probabilistic mission identification of aircraft engine usage usingnonparametric density estimation techniques, Paper GT2007-27176, Proceedings of the52nd ASMEInternational Gas Turbine&Aeroengine Technical Congress, ASME, Montreal, Canada, May14-17,2007.
[255] Enright M.P., et al.(2006).“Application of probabilistic fracture mechanics to prognosis of aircraftengine components,” AIAA Journal, AIAA,44(2), pp.311-316.
[256] Cox D.R., Oakes D., Analysis of Survival Data, Chapman and Hall:1984.
[257] Kumar, D., B. Klefjo, Proportional Hazards Model: A Review, Reliability Engineering and SystemSafety44,1994:177-188.
[258] Liao H., Zhao W., Guo H., Predicting Remaining Useful Life of an Individual Unit UsingProportional Hazards Model and Logistic Regression Model. Proceedings of the Reliability andMaintainability Symposium (RAMS),2006:127-132.
[259]左洪福,张海军,戎翔.基于比例风险模型的航空发动机视情维修决策[J].航空动力学报,2006(4):716-721.
[260] Coble J., Hines J. W., Identifying Optimal Prognostic Parameters from Data: A Genetic AlgorithmsApproach, Annual Conference of the Prognostics and Health Management Society PHM2009, SanDiego,2009.
[261] Hamada M. S., Using degradation data to assess reliability, Quality Engineering, vol.17, no.4, pp.615-620, Oct.2005.
[262] Suzuki K., Maki K., Yokogawa S.. An analysis of degradation data of a carbon film and propertiesof the estimators. In Statistical Sciences and Data Analysis: Proceedings of the Third Pacific AreaStatistical Conference, K. Matusita, Madan Lal Puri, T. Hayakawa, Ed., Utrecht, Netherlands: BrillAcademic Publishers,1993, pp.501-512.
[263] Bagdonavicius V., Bikelis A., Kazakevi ius V., Statistical analysis of linear degradation and failuretime data with multiple failure modes, Lifetime Data Analysis, vol.10, no.1, pp.65-81, Mar.2004.
[264]任淑红,左洪福,白芳.基于带漂移的布朗运动的民用航空发动机实时性能可靠性预测[J].航空动力学报,2009,24(12):2796-2801.
[265] Pecht M., Radojcic R., Rao G., Guidebook for Managing Silicon Chip Reliability, CRC Press, BocaRaton, FL,1999.
[266] Wang D., Miao Q., Kang R., Robust Health Evaluation of Gearbox Subject to Tooth Failure withWavelet Decomposition, Journal of Sound and Vibration324(3-5)(2009)1141-1157.
[267] Sun J. Z., Cheng S. F., Pecht Michael G.. Prognostics of Multilayer Ceramic Capacitors Via theParameter Residuals. IEEE Transactions on Device and Materials Reliability,2012,12(1):49-57.
[268] Sun J. Z., Zuo H. F., Wang W. B., Michael G. Pecht., Application of a state space modelingtechnique to system prognostics based on a health index for condition-based maintenance.Mechanical Systems and Signal Processing,2012,28:585-596.
[269] Hess, A., The prognostic requirement for advanced sensors and non-traditional detectiontechnologies, DARPA/DSO Prognosis Bidder’s Conference,26-27September, Alexandria, VA,2002.
[270] Virkler D. A., Hillberry B. M., Goel P. K., The statistical nature of fatigue crack propagation, Journalof Engineering Materials Technology,101(1979)148-153.
[271] Goebel, K., Eklund, N., Prognostic Fusion for Uncertainty Reduction. Proceedings of AIAAInfotech@Aerospace Conference. Reston, VA: American Institute for Aeronautics andAstronautics, Inc,2007.
[272] Papazian, J. M., Anagnostou, E. L. J., Engel, S., Fridline, D., Hoitsma, D., Madsen, J., Nardiello J.,Silberstein R. P., Welsh G.., Whiteside J.B., SIPS, A Structural Integrity Prognosis System, IEEEAerospace Conference, March3-10,2007. Big Sky, MT.
[273] Hess A., Calvello G., Frith P., Engel S.J., Hoitsma D., Challenges, Issues, and Lessons LearnedChasing the "Big P": Real Predictive Prognostics Part2, IEEE Aerospace Conference,4-11March2006, Big Sky, MT.
[274] Orchard M., Kacprzynski G., Goebel K., Saha B., Vachtsevanos G., Advances in UncertaintyRepresentation and Management for Particle Filtering Applied to Prognostics, InternationalConference on Prognostics and Health Management, Denver, CO. October6-9,2008.
[275] PHM’08conference: http://www.phmconf.org/.
[276] Gerstsbackh I. B., Kordonskiy K. B., Models of failure[M]. New York: Wiley.1969.
[277] Lu C. J., Meeker W. Q., Using degradation measures to estimate a time-to-failure distribution[J].Technometrics,1993,35(2):161–174.
[278] Crk V., Reliability assessment from degradation data [C]. Proceedings Annual Reliability andMaintainability Symposium, Washington: IEEE,2000:155–161.
[279] Hamada M., Using degradation data to assess reliability [J]. Quality Engineering,2005,17:615-620.
[280]赵建印,刘芳,孙权等.基于性能退化数据的金属化膜电容器可靠性评估[J].电子学报,2005,33(2):378-381.
[281]张永强,冯静,刘琦等.基于Poisson-Normal过程性能退化模型的可靠性分析[J].系统工程与电子技术,2006,28(11):1775-1778.
[282]彭宝华,周经伦,潘正强. Wiener过程性能退化产品可靠性评估的Bayes方法[J].系统工程理论实践,2010,30(3):543-549.
[283]尤琦,赵宇,胡广平等.基于时序模型的加速退化数据可靠性评估[J].系统工程理论实践,2011,31(2):328-332.
[284]钟强晖,张志华,梁胜杰.基于多元退化数据的可靠性分析方法[J].系统工程理论实践,2011,31(3):544-551.
[285] Upadhyaya B.R., Naghedolfeizi M., Raychaudhuri B., Residual Life Estimation of PlantComponents, P/PM Technology June,1994:22-29.
[286] Chinnam R B. On-line reliability estimation for individual components using statistical degradationsignal models [J]. Quality and Reliability Engineering International,2002,18(2):53-73.
[287] Lawless J, Crowder M., Covariates and random effects in a gamma process model with applicationto degradation and failure [J]. Lifetime Data Analysis,2004,10:213–227.
[288]赵建印,刘芳,孙权等.金属化膜脉冲电容器在线可靠性评估与性能预计[J].兵工学报,2006,27(2):266-268.
[289] Harvey A, Koopman S. J., Shephard N., State space and unobserved component models: theory andapplications [M]. Cambridge University Press,2004.
[290] de Freitas N., Rao-Blackwellised particle filtering for fault diagnosis. Proceedings of IEEEAerospace Conference,2002.
[291] Orchard M., Vachtsevanos G., A Particle Filtering Approach for On-Line Fault Diagnosis and FailurePrognosis, Transactions of the Institute of Measurement and Control, vol.31, no.3-4, pp.221-246,June2009.
[292] Yan J., Koc M., Lee J., A prognostic algorithm for machine performance assessment and itsapplication. Production Planning&Control, Vol.15, No.8,2004, pp.796–801.
[293] Doucet A, de Freitas J, Gordon N. Sequential Monte Carlo methods in practice [M]. New York:Springer-Verlag,2001.
[294] Gordon N. J., Salmond D. J., Smith A. F. M., Novel approach to nonlinear/non-Gaussian Bayesianstate estimation [J]. IEE Proceedings on Radar and Signal Processing,1993,140(2):107-113.
[295] Liu J., West M., Combined parameters and state estimation in simulation based filtering [C]. InSequential Monte Carlo Methods in Practice. Springer-Verlag, New York,2001.
[296] Carvalho, C. M., Johannes, M., Lopes, H. F., Polson, N. G., Particle learning and smoothing. Statist.Science,2010,25(1):88-106.
[297] Storvik G. Particle filters in state space models with the presence of unknown static parameters [J].IEEE Transactions on Signal Processing,2002,50(2):281-289.
[298] Polson G. N., Stroud J. R., Muller P., Practical filtering with sequential parameter learning [J].Journal of the Royal Statistical Society: Series B (Statistical Methodology),2008,70(2):413-428.
[299] Andrieu C., Doucet A., Holenstein R., Particle Markov chain Monte Carlo methods [J]. Journal ofthe Royal Statistical Society: Series B (Statistical Methodology),2010,72(3),269–342.
[300] Fearnhead, P.(2008). MCMC for state space models. Technical report, Lancaster University.
[301] Polson G., Stroud J., Muller P., Practical filtering with sequential parameter learning. Journal of theRoyal Statistical Society, Series B (Statistical Methodology)70(2008)413-428.
[302] West M., Harrison J., Bayesian prediction and dynamic models, Springer Series in Statistics.Springer-Verlag, New York,1997.
[303] Zuo M. J., Jiang R., Yam R. C., Approach for reliability modeling of continuous-state devices. IEEETransactions on Reliability,1999,48:9-18.
[304]周长春,民用涡扇发动机超温问题研究及其在使用中的预防[D].西安:西北工业大学,2006.
[305] Jardine A., Lin D., Banjevic D., A review on machinery diagnostics and prognostics implementingcondition based maintenance. Mechanical System Signal Proccessing20(2006)1483-1510.
[306] Saxena A., Goebel K. K., Turbofan Engine Degradation Simulation Data Set. Retrieved from NASAAmes Prognostics Data Repository. http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/,accessed15August2010.
[307] Frederick D., Castro J. De, J. Litt, User’s Guide for the Commercial Modular Aero-PropulsionSystem Simulation (CMAPSS). NASA/ARL,2007.
[308] Saxena A., Goebel K., Simon D., Eklund N., Prognostics Challenge Competition Summary: DamagePropagation Modeling for Aircraft Engine Run‐to‐Failure Simulation, International Conferenceon Prognostics and Health Management,2008: Denver, CO.
[309] Wang T., Yu J., Siegel D., Lee J., A similarity based prognostic approach for remaining useful lifeestimation of engineered systems. in: International conference on prognostics and heathmanagement PHM2008, Denver, USA,2008.
[310] Saxena A., Goebel K., Simon D., Eklund N., Damage propagation modeling for aircraft enginerun-to-failure simulation. in: International conference on prognostics and heath management.PHM2008,2008.
[311] Bannantine J., Comer J. J., Handrock J. L., Fundamentals of Metal Fatigue Analysis, Prentice Hall,New Jersey,1989.
[312] Goebel K., Eklund N., Prognostic Fusion for Uncertainty Reduction, Proceedings ofAIAA@Infotech Aerospace Conference, Reston, VA: American Institute for Aeronautics andAstronautics, Inc,2007.
[313] Cheng S.F., Pecht M., A Fusion Prognostics Method for Remaining Useful Life Prediction ofElectronic Products,5th Annual IEEE Conference on Automation Science and EngineeringBangalore, India, pp.102-107, August22-25,2009.
[314] Maymon G., The Problematic Nature of the Application of Stochastic Crack Growth Models inEngineering Design, Eng. Fract. Mech., Vol.53, No.6,1996, pp.911–916.
[315] Fawaz S., Equivalent Initial Flaw Size Testing and Analysis, Technical ReportAFRL-VA-WP-TR-2000-3024,2000.
[316] Luo J., Bowen P., A probabilistic Methodology for Fatigue Life Prediction, Acta Mater., vol.51, no.12, pp.3537–3550,2003.
[317] Chan K., Enright M., Probabilistic Micromechanical Modeling of Fatigue Life Variability in TiAlloy, Metall. Mater. Trans. A, vol.36, no.10, pp.2621–2631,2005.
[318] Bigerelle M., Najjar D., Fournier B., Rupin N., Iost A., Application of Lambda Distributions andBootstrap Analysis to the Prediction of Fatigue Lifetime and Confidence Intervals, Int. J. Fatigue,vol.28, no.3, pp.223–236,2006.
[319] Ebrahimi N., System Reliability Based on Diffusion Models for Fatigue Crack Growth, NavalRes.Logistics Quart., vol.52, no.1, pp.46–57,2005.
[320] Yang J., Manning S., A Simple Second Order Approximation for Stochastic Crack Growth Analysis,Eng. Fract. Mech., vol.53, no.5, pp.677–686,1996.
[321] Dolinski K., Formulation of a Stochastic Model of Fatigue Crack Growth, Fatigue Fract. Eng.Mater.Struct., vol.16, no.9, pp.1007–1019,1993.
[322] Paris P., Erdogan F., A critical analysis of crack propagation laws, Journal of Basic Engineering,85(1963)528-534.
[2] Ashok N. S., Robert W. M., Claudia M. Integrated Vehicle Health Management, Technical Plan,Version2.03.2009. National Aeronautics and Space Administration Aeronautics Research MissionDirectorate Aviation Safety Program.
[3] Asiedu Y., Gu P., Product life cycle cost analysis: state of the art review, Int.J.Prod.Res.,1998,36(N4),883–908.
[4] Bird G.., Christensen M., Lutz D., Scandura P. A., Use of integrated vehicle health management inthe field of commercial aviation, Proceedings of the1st International Forum on System HealthEngineering and Management in Aerospace–NASA ISHEM Forum2005,Napa,California,USA,7–102005, paperno.12.
[5] Alford L. D., The problem with aviation COTS [J]. Aerospace and Electronic Systems Magazine,IEEE,2001,16(2):33-37.
[6] Mercer C. R., Simon D. L., Hunter G. W. et al. Fundamental Technology Development for Gas-turbine Engine Health Management [R]. NASA-TM-2007-00223642007.
[7] Michael G. P., Prognostics and Health Management of Electronics. John Wiley&Sons.,2008.
[8] Vachtsevanos G., Lewis F. L., Roemer M., A. Hess, B. Wu. Intelligent Fault Diagnosis and Prognosisfor Engineering Systems. John Wiley&Sons,2006.
[9] Stephen B. J. et al., System Health Management: with Aerospace Applications
[10] Fox J., Glass B. J., Impact of integrated vehicle health management (IVHM) technologies on groundoperations for reusable launch vehicles (RLVs) and spacecrafts. In Proceedings of the2000IEEEAerospace Conference, Big Sky, Montana, USA,18–25March2000, vol.2, pp.179–186.
[11] Williams Z., Benefits of IVHM: an analytical approach. In Proceedings of the2006IEEE AerospaceConference, Big Sky, Montana, USA,4–11March2006, paper no.1507.
[12] Byer B., Hess A., Fila L. Writing a convincing cost benefit analysis to substantiate autonomiclogistics. In Proceedings of the2001IEEE Aerospace Conference, Big Sky, Montana, USA,10–17March2001, vol.6, pp.3095–3103.
[13] Hess, A., Fila L. Prognostics from the need to reality–from the fleet users and PHM systemdesigner/developers perspectives. In Proceedings of the2002IEEE Aerospace Conference, Big Sky,Montana, USA,9–16March2002, vol.6, pp.2791–2797.
[14] Benedettini O., Baines T. S., Lightfoot H. W., Greenough R. M., State-of-the-art in integratedvehicle health management. Journal of Aerospace Engineering,223(2), pp.157-170.
[15] Cheng S. F., Michael H. Azarian, Michael G. Pecht. Sensor Systems for Prognostics and HealthManagement. Sensors2010,10,5774-5797.
[16] Prosser W. H.; Brown T. L.; Woodard S. E.; Fleming, G. A, et al., Sensor Technology for IntegratedVehicle Health Management of Aerospace Vehicles. REVIEW OF PROGRESS INQUANTITATIVE NONDESTRUCTIVE EVALUATION:Volume22. AIP Conference Proceedings,Volume657, pp.1582-1589,2003.
[17] Nezih M., State of Development of Advanced Sensory Systems for Structural Health MonitoringApplications, Proceedings of the NATO RTO AVT-144Workshop on Enhanced Aircraft PlatformAvailability Through Advanced Maintenance Concepts and Technologies, Vilnius, Lithuania,3-5October2006(DRDC Atlantic SL-2008-260).
[18] Meredith K., Safai M., Georgeson G., New MEMS technologies for integrated vehicle healthmanagement and fluid sensing applications. IEEE Aerospace conference,2009.
[19] SAE E-32Aerospace Propulsion Systems Health Management Committee. Prognostics for GasTurbine Engines. SAE Standard AIR5871,2008.
[20] Papazian J. M., Anagnostou E. L., Engel S. J. A. et al., structural integrity prognosis system.Engineering Fracture Mechanics76(2009)620-632.
[21] Roemer M. J., Dzakowic J., Orsagh R.F., Validation and Verification of Prognostic and HealthMangement Technologies. IEEE Aerospace conference,2004.
[22] Reed E., Schumann J., Mengshoel O. J., Verification and Validation of System Health ManagementModels Models using Parametric Testing. AIAA-2011-1445,2011.
[23] Saxena A., Celaya J., Balaban E., Saha B., Saha S., Goebel K., Metrics for evaluating performanceof prognostic techniques, International Conference on Prognostics and Health Management(PHM08),2008, Denver CO, pp.1-17.
[24] Hess A., Prognostics and Health Management-A Thirty-Year Retrospective, Joint Strike FighterProgram Office, http://ti.arc.nasa.gov/projects/ishem/papers_pres.php.
[25] Hess A., The Prognostic Requirement for Advanced Sensors and Non-Traditional DetectionTechnologies. DARPA/DSO Prognosis Bidder’s Conference September26-27,2002, Alexandria,VA.
[26]王克昌.液体火箭发动机的健康管理系统[J].上海航天,1992(1):27-35.
[27]郑哲敏,赵亚溥. PHM——机械失效的预测和安全管理系统[J].力学进展,1999,29(2):268,243.
[28]孙博,康锐,谢劲松.故障预测与健康管理系统研究和应用现状综述[J].系统工程与电子技术,2007,29(10):1762-1767.
[29]张宝珍.预测与健康管理技术的发展及应用[J].测控技术,2008,27(2):5-7.
[30]常琦,袁慎芳.飞行器综合健康管理(IVHM)系统技术现状及发展[J].系统工程与电子技术,2009,31(11):2652-2657.
[31]马宁,吕琛.飞机故障预测与健康管理框架研究[J].华中科技大学学报（自然科学版）,2009,37(1):207-209（增刊）.
[32]李文娟,马存宝,贺尔铭.综合飞行器健康管理系统组成框架及关键技术研究[J].航空工程进展,2011,2(3):330-334.
[33] Urban L.A., Gas Path Analysis Applied to Turbine Engine Conditioning Monitoring. AIAA/SAEPaper1972,72-1082.
[34] Urban L.A. Parameter selection for multiple fault diagnostics of gas turbine engines. AGARDConference Proceedings, No.165,1974, Zurich, Switzerland.
[35] Wade R. A., A Need-focused Approach to Air Force Engine Health Management Research,Proceedings of the2005IEEE Aerospace Conference, Big Sky, Montana, March2005.
[36] Jaw L. C., Recent Advancements in Aircraft Engine Health Management (EHM) Technologies andRecommendations for the Next Step. ASME Turbo Expo2005: Power for Land, Sea and Air, June,2005.
[37] SAE. E-32Committee Fact Sheet: SAE Technical Committee E-32Aerospace Propulsion SystemsHealth Management, http://www.sae.org/servlets/works/committeeHome.do?comtID=TEAE32.
[38] SAE. Aircraft Gas Turbine Engine Health Management System Guide, SAE ARP1587, Revison B,2007.
[39] Fisher C. E., Data and information fusion for gas path debris monitoring, IEEE AerospaceConference Proceedings,2001.
[40] Powrie H. E. G., Novis A., Gas path debris monitoring for F-35Joint Strike Fighter propulsionsystem PHM, IEEE Aerospace Conference Proceedings,2006.
[41] Tappert P., von Flotow A., Mercadal M., Autonomous PHM with blade-tip-sensors: algorithms andseeded fault experience, Aerospace Conference,2001, IEEE Proceedings,2001.
[42] von Flotow A., Mercadal M., Tappert P., Health monitoring and prognostics of blades and disks withblade tip sensors.2000IEEE Aerospace Conference Proceedings, Big Sky, MT, USA.
[43] Deol D. L., TEMPER—A gas-path analysis tool for commercial jet engines. J. Eng. Gas TurbinesPower, Trans ASME,1994,116,82–89.
[44]范作民孙春林白杰.航空发动机故障诊断导论[M].北京科学出版社2004.
[45] Stamatis A., Mathioudakis K., Smith M., Papailiou K., Gas turbine component fault identification bymeans of adaptive performance modeling. ASME Paper90-GT-376,1990.
[46] Doel D. L., Interpretation of Weighted-Least-Squares Gas Path Analysis Results, Transactions of theASME,2005, Vol.125:624-633.
[47] Volponi A. J., Gas path analysis: An approach to engine diagnostics.35th Symposium MechanicalFailures Prevention Group, Gaithersbury, MD, April1982.
[48] Provost M. J. COMPASS: A generalized ground-based monitoring system. AGARD-CP-449, EngineCondition Monitoring—Technology and Experience, October1988.
[49] Urban L. A., Volponi A. J., Mathematical methods of relative engine performance diagnostics, SAE1992Transactions Journal of Aerospace, Section1, Vol.101, SAE Technical Paper No.922048,1992.
[50] Dewallef P., Leonard O., Mathioudakis K., On-line aircraft engine diagnostic using asoft-constrained kalman filter [C]. ASME Paper No.GT2004-53539,2004.
[51] Aretakis N., Mathioudakis K., Stamatis A., Non-Linear Engine Component Fault Diagnosis From aLimited Number of Measurements Using a Combinatorial Approach, ASME J. Eng. Gas TurbinesPower,2003,125, pp.642–650.
[52] Mathioudakis K., Kamboukos Ph., Stamatis A., Gas turbine component fault detection from alimited number of measurements [C]. Proc. Inst. Mech. Eng., Part A,2004,218:609–618.
[53] Torella G.., Lombardo G.., Neural Networks for the diagnostics of gas turbine engines. ASME96-TA-39, ASME Turbo Asia Conference,1996.
[54] Romessis A., Stamatis A., Mathioudakis K., A parametric investigation of the diagnostic ability ofprobabilistic neural networks on turbofan engines. ASME2001-GT-0011, ASME Turbo Expo2001,New Orleans, Louisiana, June2001.
[55] Dong-Hyuck Seo, Tae-Seong Roh, Dong-Whan Choi. Defect diagnostics of gas turbine engine usinghybrid SVM-ANN with module system in off-design condition. JOURNAL OF MECHANICALSCIENCE AND TECHNOLOGY,2009, Volume23, Number3,677-685.
[56]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,2005, Vol.26（6）：686-690.
[57] Tang G.., Yates C. L., Zhang, J., Chen D., A practical intelligent system for condition monitoring andfault diagnosis of jet engines. AIAA99-2533,1999.
[58] Ganguli R., Application of fuzzy logic for fault isolation of jet engines. ASME2001-GT-0013,ASME Turbo Expo2001, New Orleans, Louisiana, June2001.
[59] Vivian, B., Singh, R., Application of expert system technology to gas path analysis of a single shaftturboprop engine.5th European Propulsion Forum, Pisa, Italy,5–7April1995.
[60] Zhernakov S. V., Diagnostics and Checking of Gas-Turbine Engines Parameters with Hybrid ExpertSystems, Proceedings of the Workshop on Computer Science and Information Technologies,2000.
[61] Jaw L., Neural Networks for Model-based Prognostics, Proceedings of IEEE Aerospace Conference,Aspen, Colorado, March1999.
[62] Volponi A., Data Fusion for Enhanced Aircraft Engine Prognostics and Health Management, NASA/CR-2005-214055, E-15412,2005.
[63] Harvey T. J., Wood R. J. K., Powrie H. E. G., Electrostatic wear monitoring of rolling elementbearings. Wear,2007,263:1492-1501.
[64] Barragan J. A. M., Engine Vibration Monitoring and Diagnosis Based on On-board Captured Data,NATO AVT Symposium on Aging Mechanism and Control, Manchester, UK,2001.
[65] Orsagh R. F., Sheldon J., Klenke C. J.; Prognostics/Dignostics for Gas Turbine Engine Bearings,Proceedings of IEEE Aerospace Conference, Big Sky, Montana, March2003.
[66] Simon D., et al., Sensor Needs for Control and Health Management of Intelligent Aircraft Engines,Proceedings of ASME Turbo Expo2004; GT2004-54324.
[67] Hudak S. J., et al., A Probabilistic Analysis of the Benefits of In-Service Fatigue DamageMonitoring for Turbine Engine Prognosis.45th AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics&Materials Conference, April2004.
[68] Hudak S. J., Enright P. M., McClung C. R., et al. Enhanced Life Prediction Technology for EngineRotor Life Extension (ERLE). AFRL-RX-WP-TR-2008-4287,2008.
[69] Holtz C., Smith G., Friend R., Modernizing systems through data integration-A vision for EHM inthe United States Air Force [A]. Proceeding of AIAA/ASME/SAE/ASEE40th Joint PropulsionConference and Exhibit [C]. Fort Lauderdale, FL. AIAA, Inc,2004:1-12.
[70] www.wpafb.af.mil/news/story_print.asp?id=123221807
[71] Clark G. J., Vian J. L., West M. E., et al., Multi-platform Airplane Health Management [C].IEEEAerospace onference.2007:1-13.
[72]费成巍,艾延廷.航空发动机健康管理系统设计技术[J].航空发动机,2009,35(5):24-29
[73]姜彩虹,孙志岩,王曦.航空发动机预测健康管理系统设计的关键技术[J].航空动力学报,2009,24(11):2589-2594.
[74]王施,王荣桥,陈志英,樊江,申秀丽.航空发动机健康管理综述[J].燃气涡轮试验与研究,2009,22(1):51-58.
[75]文振华,左洪福,李耀华.气路颗粒静电监测技术及实验[J].航空动力学报,2008,23(12):2321-2326.
[76]李耀华,左洪福,刘鹏鹏.某型航空涡轮轴发动机尾气静电监测探索性实验[J].航空学报,2010,31(11):2174-2181.
[77]孙见忠,左洪福,詹志娟,刘鹏鹏.涡轴发动机尾气静电信号影响因素分析[J].航空学报,2012,32(3):412-420.
[78]陈志雄,左洪福,詹志娟,张营.滑油系统全流量在线磨粒静电监测技术研究[J].航空学报,2012,32(3):437-443.
[79]段发阶,方志强,孙宇扬,叶声华.叶尖定时旋转叶片实时振动测量技术.光电工程,2005,Vol.32, No.328-31.
[80]段发阶,张玉贵,等.航空发动机旋转叶片振动监测系统研究.光学与光电技术,2008, Vol.6,No.148-51.
[81]陈卫,程礼,李全通,高卫星,航空发动机监控技术[M].北京：国防工业出版社,2011.
[82]李长征雷勇.航空发动机气路故障诊断[J].测控技术200625(8):21-24.
[83]蒋亮,李书明,郝英,等.航空发动机气路故障诊断研究现状[J].中国民航飞行学院学报200523(增刊)60-65.
[84]陈恬,孙健国,郝英.基于神经网络和证据融合理论的航空发动机气路故障诊断[J].航空学报200627(6):1014-1017.
[85]张鹏,黄金泉.航空发动机气路故障诊断的平方根UKF方法研究[J].航空动力学报,2008.
[86] Chen G., Yang Y. W., Zuo H. F., Intelligent Fusion for Aero engine Wear Fault Diagnosis,Transactions of Nanjing University of Aeronautics&Astronautics,2006,23(4):297-303.
[87]陈恬,孙健国,郝英,基于神经网络和证据融合理论的航空发动机气路故障诊断,航空学报,2006,27（6）：1014-1017.
[88]杨建平,黄洪钟,苗强等,基于证据理论的航空发动机早期故障诊断方法,航空动力学报,2008,23（12）：2327-2331.
[89]曲建岭,唐昌盛,肖辉雄等,人工神经网络融合诊断航空发动机气路故障,航空动力学报,2008,23（11）：2124-2127.
[90]鲁峰,黄金泉,陈煜,航空发动机部件性能融合故障诊断方法研究,航空动力学报,2009年第7期.
[91]黄伟斌.发动机健康管理的自适应机载实时模型[D].[硕士论文],南京:南京航空航天大学,2007.
[92]袁春飞,姚华,杨刚,航空发动机机载实时自适应模型研究,航空学报,2006,27（4）：561-564.
[93]陈果,用结构自适应神经网络预测航空发动机性能趋势[J].航空学报2007,28(3)535-539.
[94]胡金海,谢寿生,骆广琦等,基于支持向量机方法的发动机性能趋势预测[J].推进技术200526(3)260264.
[95]任淑红,左洪福.基于性能衰退的航空发动机剩余寿命组合预测方法[J].机械科学与技术,2011,30(1):23-29.
[96]吕永乐,郎荣令,路辉等,航空发动机性能参数联合RBFPN和FAR预测[J].北京航空航天大学学报,2010,36(2):131-134.
[97]张宝诚,刘孝安.航空发动机可靠性和经济性[M].北京:国防工业出版社,1998.
[98]王通北,陈美英.军用航空发动机的可靠性和寿命[J].航空发动机,1994(1):36-47.
[99]洪杰,张大钧,韩继斌.航空发动机关键件使用寿命监视系统设计[J].北京航空航天大学学报,2000,26(1):45-48.
[100]甘晓华,李伟.现役航空发动机使用寿命确定和控制方法[J].航空工程进展,2010,1(2):103-106.
[101] Litt J. S., Simon D. L., A Survey of Intelligent Control and Health Management Technologies forAircraft Propulsion Systems. JOURNAL OF AEROSPACE COMPUTING, INFORMATION, ANDCOMMUNICATION, Vol.1, pp.543-563,2004.
[102] Jaw L., Mattingly J., Aircraft engine controls: design, system analysis, and health monitoring. AIAA,2009.
[103] Tu F., Ghoshal S., Luo J., et al., PHM Integration with Maintenance and Inventory ManagementSystems. IEEE Aerospace Conference.2007.
[104] Camci F., Valentine G. S., Navarra K., Methodologies for Integration of PHM Systems withMaintenance Data. IEEE Aerospace Conference.2006.
[105]钟诗圣,栾圣罡.面向航空发动机全寿命周期管理的航线数据处理系统[J].计算机集成制造系统,2006,12(8):1273-1278.
[106]戎翔.民航发动机健康管理中的寿命预测与维修决策方法研究[D].[博士论文],南京：南京航空航天大学,2008.
[107]姚捷.浅谈CFM56-5B4航空发动机状态监控[J].江苏航空,2011,123(3):35-36.
[108]涂杰,苏有生,张雷.2007年度国内运输航空器发动机使用和空中停车统计分析[J].中国民用航空,2008,88:54-56.
[109]黄郁华.发动机空中停车的故障分析与预防措施[J].民航经济与技术，1996,173:35-37.
[110]李丹,段宝君.不同寿命控制方式下单元体发动机大修经济性分析[J].航空维修,2004,2:45-46.
[111]于晓伟,邢晨光,王萍,张宝珍. F119发动机的维修保障模式, http://home.cetin.net.cn/qrms/file/201001/upFiles/2f7edd82-bc2e-4853-9878-882862f9da84.pdf.
[112]张萍,航空发动机单元体设计的维护实践[J].民用飞机设计与研究,37-40.
[113]成国伟,毛红军,任淑红.民用航空发动机外包维修成本的控制[J].江苏航空,2008,3:20-22.
[114]梁剑,左洪福,常继百,等.基于统计粗集模型的航空发动机维修等级预测方法研究[J].应用科学学报,2005,23(2):196-199.
[115]张海军,左洪福,梁剑.基于信息熵属性约简的航空发动机送修等级决策[J].系统工程,2005,23(7):105-108.
[116]付旭云,钟诗胜,丁刚.民用航空发动机单元体送修工作范围决策[J].航空动力学报,2010,25(10):2195-2200.
[117]徐可君,江龙平.军用航空发动机可靠性和寿命管理[J].中国工程科学,2003,5(1):82-88.
[118] GEAE/CFMI, GE Aircraft Engines Monitoring Training Course Material.
[119] GE, DiagnosticsTrend Interpretation Training Material,2007.
[120] West M., Harrison P. J., Bayesian forecasting and dynamic models [M].2nd Edn, Springer-Verlag,New York,1997.
[121] Petris G., Petrone S., Campagnoli P., Dynamic Linear Models with R [M]. Springer-Verlag, NewYork,2009.
[122]林兆福,范作民.发动机基线方程的建立和应用.中国民航学院学报.1992,12,10(4)：20-32.
[123]钟诗胜,崔智全,付旭.Rolls-Roycs发动机基线挖掘方法.计算机集成制造系统.2010.10.16(10)：2265-2270.
[124]钟诗胜,周志波,张永,康力平.基于三次回归分析的试车台基线库的建立[J].计算机集成制造系统,2005,11(2).
[125] TEACHES: Turbine engine advance calculation and health assessment educational software(turbofan engine version), National Technical university of Athens.
[126] Dewallef P., Application of the Kalman filter to health monitoring of gas turbine engines: asequential approach to robust diagnosis. PhD Thesis, University of Liège,2005.
[127] The R Project for Statistical Computing. http://www.r-project.org/.
[128] Isermann R., Balle P., Trends in the Application of Model-Based Fault Detection and Diagnosis ofTechnical Processes,Control Eng. Practice, vol.5, no.5,1997, pp.709-719.
[129] Hines J. W., Garvey D., Seibert R., Usynin A., Technical review of on-line monitoring techniquesfor performance assessment, Volume2: Theoretical issues. U.S. Nuclear Regulatory Commission,Washington, DC, NUREG/CR-6895,2007
[130] SINGER R.M., GROSS K.C., HERZOG J.P., KING R.W., WAGERICH S.W., Model-BasedNuclear Power Plant Monitoring and Fault Detection: Theoretical Foundations, Proc.9th Intl. Conf.on Intelligent Systems Applications to Power Systems, Seoul, Korea,(1996).
[131] GRIBOK A.V., Hines J.W., Uhrig R.E., Use of Kernal Based Techniques for Sensor Validation inNuclear Power Plants, The Third American Nuclear Society International Topical Meeting onNuclear Plant Instrumentation and Control and Human-Machine Interface Technologies, WashingtonDC, November13-17,2000.
[132] GRIBOK A.V., Hines J.W., Urmanov A.M., Uncertainty Analysis of Memory Based SensorValidation Techniques, Real Time Systems,2004,27(1),7-26.
[133] Bickford R. L., et al., MSET Signal Validation System for Space Shuttle Main Engine, Final Report,NASA Contract NAS8-98027,2000,8.
[134] Bickford R. L., Meyer C., Lee V., Online Signal Validation for Assured Data Quality, Proc. of the2001Instrument Society of America,2001.
[135] Gross K. C., Lu W., Early Detection of Signal and Process Anomalies in Enterprise ComputingSystems, Proc.2002IEEE Int’l Conf. on Machine Learning and Applications (ICMLA), Las Vegas,NV, June2002.
[136] Wegerich S., Similarity Based Modeling of Time Synchronous Averaged Vibration Signals forMachinery Health Monitoring, Proceedings, IEEE Aerospace Conference, Big Sky, MT, USA,March6-13,2004.
[137] Wegerich S., Similarity Based Modeling of Vibration Features for Fault Detection and Identification,Sensor Review, Vol.25, Issue2,2005.
[138]姚良,李艾华,孙红辉,张振仁.基于MSET和SPRT的内燃机气阀机构振动监测[J].振动工程学报,2009,22(2):150-155.
[139] Black L. C., Uhrig R. E, Wesldy H. J., System modeling and instrument calibration verification witha nonlinear state estimation technique[A]. Proceedings of the Maintenance and ReliabilityConference[C]. Knoxville, TN, May,1998.
[140] Cherkassky V., Mulier F., Learning From Data, John Wiley&Sons,1998.
[141] Hines J. W., Garvey D., Traditional and Robust Vector Selection Methods for Use with SimilarityBased Models,5thInternational Topical Meeting on Nuclear Plant Instrumentation, Control andHuman-Machine Interface Technologies, Albuquerque, NM,2006.
[142] Hines, J. W., Usynin A., MSET Performance Optimization Through Regularization, NuclearEngineering and Technology,38(2)(April2005).
[143]葛忠汉.飞机发动机空停事件及预防[J].航空维修工程,2009,1:46-48.
[144] Couch R. P., Rossback D. R., Burgess R. W., et al., Sensing incipient engine failure withelectrostatic probes [C]//Instrumentation for Airbreathing Propulsion, Proceeding of theSymposium.1972:519-529.
[145] Couch R. P., Detecting abnormal turbine engine deterioration using electrostatic methods, Journal ofAircraft,1978,15(10):692-695.
[146] Fisher C. E., Gas path condition monitoring using electrostatic techniques, Engine ConditionMonitoring: Technology and Experience,1988.
[147] Powrie H. E. G., Mcnicholas K., Gas path condition monitoring during accelerated mission testingof a demonstrator engine, AIAA-1997-2904,1997.
[148] Powrie H. E. G., Fisher C. E., Engine health monitoring: Towards total prognostics, IEEE AerospaceConference Proceedings,1999.
[149] Fisher C. E., Gas path debris monitoring-a21st century PHM tool, IEEE Aerospace ConferenceProceedings,2000.
[150] Fisher C. E., Gas turbine condition monitoring systems—an integrated approach, IEEE AerospaceConference Proceedings,2000.
[151]文振华,左洪福,王华等.航空发动机气路静电监测传感器特性[J].传感器与微系统,2008,27(11):28-31.
[152]李耀华,左洪福.碰摩故障静电监测方法及模拟实验[J].航空学报,2010,31(6):1156-1163.
[153]金如山.航空燃气轮机燃烧室[M].北京:宇航出版社,1988.
[154] Frenklach M., Clary D. W., Gardiner Jr W. C., Stein S. E., Detailed kinetic modeling of sootformation in shock-tube paralysis of acetylene. Process to Combustion Institute,20:887-901,1985.
[155] Bansal G., Mueller M. E., Pitsch H., Direct numerical simulation of soot formation in jet-enginecombustors. Center for Turbulence Research Annual Research Briefs2009.
[156]李红红.航空发动机二维模型燃烧室中碳黑颗粒生成数值模拟.南京航空航天大学,硕士论文,2008.
[157] Lefebvre A H., Gas turbine combustion, Washington, Hemisphere Pub. Corp.,1983.
[158] SAE. Aircraft Gas Turbine Exhaust Measurements, SAE ARP1179, Revision B, SAE, Warrendale,PA.1991.
[159] Wilson C. W., Petzold A., Nyeki S., Schumann U., Zellner R., Measurement and Prediction ofEmissions of Aerosols and Gaseous Precursors from Gas Turbine Engines (PartEmis): An Overview.Aerospace Science and Technology, Vol.8, No.2,2004, pp.131–143.
[160] Petzold A., et al., Particle emissions from aircraft engines–a survey of the European projectPartEmis. Meteorologische Zeitschrift,2005,14(4),465-476.
[161] Wey C.C., Anderson B.E., et al., Aircraft Particle Emissions Experiment (APEX). NASA/TM-2006-214382, ARL-TR-3903. U.S. National Aeronautics and Space Administration, Glenn ResearchCenter, Cleveland, OH. Available at: http://gltrs.grc.nasa.gov,2006.
[162] Kinsey J. S., Characterization of Emissions from Commercial Aircraft Engines During the AircraftParticle Emissions Experiment (APEX)1to3. EPA-600/R-091/130. U.S. Environmental ProtectionAgency, Research Triangle Park, NC,2009.
[163] Kinsey J., Dong Y., Williams D. C., Logan R., Physical characterization of the fine particleemissions from commercial aircraft engines during the Aircraft Particle Emissions eXperiment(APEX)1-3. Atmospheric Environment2010,44:2147-2156.
[164] Kinsey J., Hays M., Dong Y., Williams D., Logan R., Logan, Chemical Characterization of the FineParticle Emissions from Commercial Aircraft Engines during the Aircraft Particle EmissionseXperiment (APEX)1to3. Environ Science Technology.2011,45(8):3415-21.
[165] Onasch T. B., Jayne J. T., Herndon S., Worsnop D. R., MiakeLye R. C., Mortimer I. P., Anderson B.E., Chemical properties of aircraft engine particulate exhaust emissions, Journal of Propulsion andPower,25,1121–1137.
[166] Timko M. T., Onasch T. B., Northway M. J., Jayne J. T., Canagaratna M. R., Herndon S. C., Wood E.C., Miake-Lye R. C., Knighton W. B.. Gas turbine engine emissions Part II: Chemical properties ofparticulate matter [J]. J. Eng. Gas Turb. Power,2010,132,061505(15pages).
[167] Whitefield P. D., et al., Summarizing and Interpreting Aircraft Gaseous and Particulate EmissionsData. Airport Cooperative Research Program Report9, Transportation Research Board,2008.
[168] Dopelheuer A., Lecht M., Influence of Engine performance on Emissions Characteristics [C].Symposium of the applied vehicle Technology Pane-Gas Turbine Engine Combustion, Emissionsand alternative fuels, Lisbon, Portugal,1998.
[169]陈锐,周彤,顾铭企.某型发动机主燃烧室积碳的排除[J].航空发动机,1996(3).
[170]姚四伟,张力先,霍岩.发动机燃烧室部件故障的分析与预防[J].西安航空技术高等专科学校学报,2008,26(1):18-20.
[171] Chupp R. E., Ghasripoor F., Moore G. D., et al., Applying Abradable Seals to Industrial GasTurbines,2002GRC193,2002.
[172] Kennedy Jr F. E., Thermomechanical effects in high-speed seal rubs, NASA-CR-180418,1987.
[173] Chappel D., Howe H., Vo L., Abradable Seal Testing: Blade Temperatures During Low Speed RubEvent,2001-3479,2001.
[174] Wang H., Criteria for analysis of abradable coatings, Surface&coatings technology,1996,79(1-3):71-75.
[175]宋兆泓,陈光,张景武,洪其麟.航空发动机典型故障分析[M].北京航空航天大学出版社,1993.
[176] Di Domenico M., Gerlinger P., Aigner M.. Development and validation of a new soot formationmodel for gas turbine combustor simulations. Combustion and Flame,2010,157:246–258.
[177] Bansal G., Mueller M. E., Pitsch H., Direct numerical simulation of soot formation in jet-enginecombustors. Center for Turbulence Research Annual Research Briefs2009.
[178]李红红.航空发动机二维模型燃烧室中碳黑颗粒生成数值模拟.南京航空航天大学,硕士论文,2008.
[179]柴志刚.某涡轴发动机Ⅰ级涡轮叶片叶尖涂层剥落失效分析[J].失效分析与预防,2007,2(1):
[180]梁剑,左洪福,常继百,赵红华,周左成,基于统计粗集模型的航空发动机维修等级预测方法研究.应用科学学报,2005,23(2):196~199
[181]付旭云,钟诗胜,丁刚,民用航空发动机单元体送修工作范围决策[J].航空动力学报,2010(10):2195‐2200.
[182]徐可君,基于单元体的军用航空发动机寿命控制和管理.海军航空工程学院学报,2007,22(5):541-547.
[183] Turevskiy A., Meisner R., Luppold R., Kern R., Fuller J., A Model Based Controller for CommercialAero Gas Turbines, ASME paper GT-2002-30041,2002.
[184] Lietzau K., Kreiner A., Model Based Control Concepts for Jet Engines, ASME paper2001-GT-0016,2001.
[185] Kamboukos Ph., Mathioudakis K., Comparison of linear and non-linear gas turbine performancediagnostics. Journal of Engineering for Gas Turbines and Power,2005, Vol.127,49-56.
[186] Gulati A, Taylor D., Singh R., Multiple operating point analysis using genetic algorithmoptimization for gas turbine diagnostics [C]. XV ISABE, Bangalore, India, Sept.3–7, Paper No.ISABE2001-1139,2001.
[187] Kamboukos P., Mathioudakis K., Multipoint Non-linear Method for Enhanced Component andSensor Malfunction Diagnosis, ASME Paper No. GT-2006-90451,2006.
[188] Luppold R.H., Gallops G.W., Kerr L.J., Roman J.R. et al., Estimating In-Flight Engine PerformanceVariations Using Kalman Filter Concepts, AIAA-89-2584,1989.
[189] Kerr L. J., Nemec T. S., Gallops G W., Real Time Estimation of Gas Turbine Engine Damage Usinga Control Based Kalman Filter Algorithm, ASME91-GT-216,1991.
[190] Tsalavoutas A., Pothos S., Mathioudakis K., Stamatis A., Monitoring of the Performance of a TwinSpool Ship Propulsion Turbine by Means of Adaptive Modeling, RTO Symposium on Gas TurbineOperation and Technology for Land, Sea and Air Propulsion and Power Systems, Ottawa, Canada,October18–21, Paper No. RTO-MP-34,1999.
[191] Lambiris B., Mathioudakis K., Stamatis A., Papailiou K. D., Adaptive Modeling of Jet EnginePerformance With Application to Condition Monitoring, J. Propul. Power,10(6), pp.890–896,1994.
[192] Lee Y., Singh R., Health Monitoring of Turbine Engine Gas Path Components and MeasurementInstruments, ASME Paper No.96-GT-242,1996.
[193]张鹏,黄金泉,基于双重卡尔曼滤波器的发动机故障诊断,航空动力学报,2008年第5期.
[194] Takahisa Kobayashi, Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank ofKalman Filters, NASA/CR-2003-212298.
[195] Depold H. R., Gass F. D., The application of expert systems and neural networks to gas turbineprognostics and diagnostics. ASME Paper98-GT-101. J. Eng. Gas Turbines and Power, TransASME,1999,121,607–612.
[196] Ganguli R., Data rectification and detection of trend shifts in jet engine gas path measurementsusing median filters and fuzzy logic. ASME2001-GT-0014, ASME TURBO EXPO2001, NewOrleans, Louisiana, June2001.
[197] Zedda M., Singh R., Gas turbine engine and sensor fault diagnosis using optimisationtechniques.AIAA-99-2530,1999.
[198] Nielsen H. B., Immoptibox: A MATLAB TOOLBOX FOR OPTIMIZATION AND DATA FITTING.www2.imm.dtu.dk/~hbn/immoptibox/.
[199] Mathioudakis K., Kamboukos P., Assessment of the Effectiveness of Gas Path Diagnostic Schemes,ASME J. Eng. Gas Turbines Power,128(1), pp.57–63,2006.
[200] Olsson W. J., Stromberg W. J., Aircraft Engine Diagnostics, NASA Lewis Res. Center, pp.43–61,Jan.1981, Contract No. NASA3-20632,1981.
[201] Fasching W. A., Stricklin, R., CF6Jet Engine Diagnostics Program: Final Report,NASA/CR-165582,1982.
[202] Sasahara O., JT9D engine/module performance deterioration results from back to back testing.Turbomachinery Perform Deterioration,25–32,1986.
[203] Crosby J., Factors J., Relating to deterioration based on Rolls-Royce RB211in service performance.Turbomachinery Perform Deterioration,41–47,1986.
[204] Nilkitsaranont Y., Li P., Gas turbine performance prognostic for condition-based maintenance.Applied Energy862152-2161,2009.
[205] Roemer, M.J., Kacprzynski, G.. J., Schoeller, M. H., Improved diagnostic and prognosticassessments using health management information fusion. AUTOTESTCON Proceedings,2001.IEEE Systems Readiness Technology Conference, Page(s):365–377.
[206] Eklund N. H., Goebel K. F., Using Neural Networks and the Rank Permutation Transformation toDetect Abnormal Conditions in Aircraft Engines, IEEE Mid-Summer Workshop on Soft Computingin Industrial Applications, June2005, IEEE0-7803-8942-5, pp.1-5,2005.
[207] Yan W. Z., Kai G., Sensor Validation and Fusion for Gas Turbine Vibration Monitoring[R]. America:GE Global Research,2003.
[208] Alag S., Agogino A. M., Morjaria M., A methodology for intelligent sensor measurement, validation,fusion, and fault detection for equipment monitoring and diagnostics. Artificial Intelligence forEngineering Design, Analysis and Manufacturing,2001,15,307–320.
[209] Donat W., Choi K., An W., et al, Data Visualization, Data Reduction and Classifier Fusion forIntelligent Fault Detection and Diagnosis in Gas Turbine Engines. ASME Turbo Expo2007: Powerfor Land, Sea, and Air (GT2007) May14–17,2007, Montreal, Canada.
[210] Li C., Lei Y., Fault Diagnosis for an Aircraft Engine Based on Information Fusion.2006IEEEInternational Conference on Mechatronics,2006,7Page:199-202.
[211] Sun J. Z., Zuo H. F., Yang H. B., Pecht M.. Study of Ensemble Learning-Based Fusion Prognostics.2010Prognostics&System Health Management Conference (PHM2010Macau), Jan.2010, page1-7.
[212] Alexander P., Singh R., Gas Turbine Engine Fault Diagnostics Using Fuzzy Concepts, AIAA1stIntelligent Systems Technical Conference, Sept.2004, AIAA2004-6223, pp.1-15.
[213]陈果.航空器检测与诊断技术导论.中国民航出版社,2007.
[214] Kyriazis A., Tsalavoutas A., Mathioudakis K., Gas Turbine Fault Identification by Fusing VibrationTrending and Gas Path Analysis. ASME Turbo Expo2009: Power for Land, Sea, and Air (GT2009)June8–12,2009, Orlando, Florida, USA.
[215] Breese J. S., Horvitz E. J., Peot M. A.., Gay R., Quentin, G.. H., Automated Decision-AnalyticDiagnosis of Thermal Performance in Gas Turbines, ASME Paper No.92-GT-399,1992.
[216] Palmer C. A., Combining Bayesian Belief Networks With Gas Path Analysis for Test CellDiagnostics and Overhaul, ASME Paper No.98-GT-168,1998.
[217] Romessis C., Stamatis A., Mathioudakis K., Setting up a Belief Network for Turbofan DiagnosisWith the Aid of an Engine Performance Model, ISABE Paper2001, No.1032,2001.
[218] Romessis C., Mathioudakis K., Bayesian Network Approach for Gas Path Fault Diagnosis, ASME J.Eng. Gas Turbines Power,128(1), pp.64–72,2006.
[219] Lee Y. K., Mavris D. N., Volovoi V. V., et al. A Fault Diagnosis Method for Industrial Gas TurbinesUsing Bayesian Data Analysis. Journal of Engineering for Gas Turbines and Power,2010, Vol.132.
[220] Lee Y. K., Mavris D. N., Volovoi V. V., M. Yuan. A Bayesian method for calibrating computermodels to test data. Inverse Problems in Science and Engineering,2011,19(3):395-408.
[221] Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. MorganKaufman, San Mateo, CA,1988.
[222] Stephenson T A. An Introduction to Bayesian Network Theory and Usage. IDIAP-RR00,03, Feb,2000.
[223] Nilsson N. Artificial Intelligence: A New synthesis Morgan Kaufmanm Publishers, USA,1998.
[224] Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equations of state calculations by fastcomputing machines[J]. Journal of Chemical Physics,1953,21:1087-1091.
[225] Hastings W K. Monte Carlo sampling methods using Markov chains and their application [J].Biometrika,1970,57:97-109.
[226] Geman S and Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration ofimages[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6:721-741.
[227] Lunn D. J., Thomas A., Best N., Spiegelhalter D., WinBUGS-a Bayesian modelling framework:concepts, structure, and extensibility. Statistics and Computing,10:325—337,2000.
[228] Meeker W Q, Escobar L A. Statistical method for reliability data[M]. John Wiley and Sons,1998.
[229] Stephen B., Johnson, T. G., Seth K., System Health Management: with Aerospace Applications.Wiley,2011.
[230] Pecht M. G., Prognostics and health management of electronics. Wiley-Interscience, New York,2008.
[231] Engine Roter Life Extension. http://www.ml.afrl.af.mil/mll/b-metals.html.
[232] Kothamasu R., Huang S.H., VerDuin W.H., System health monitoring and prognostics-a review ofcurrent paradigms and practices, International Journal of Advanced Manufacturing Technology28,20061012-1024.
[233] Heng A., Zhang S., Tan A.C.C., Mathew J., Rotating machinery prognostics: state of the art,challenges and opportunities, Mechanical Systems and Signal Processing23(2009)724-739.
[234] Ray A., Tangirala S., Stochasticmodeling of fatigue crack dynamics for on-line failure prognostics,IEEE Transactions on Control Systems Technology4(1996)443-451.
[235] Kacprzynski G. J., Sarlashkar A., Roemer M.J., Predicting remaining life by fusing the physics offailure modeling with diagnostics, Journal of Metal56(2004)29-35.
[236] Li C. J., Lee H., Gear fatigue crack prognosis using embedded model, gear dynamic model andfracture mechanics, Mechanical Systems and Signal Processing19(2005)836-846.
[237] Valentin R., Osterman M., Newman B., Remaining life assessment of aging electronics in avioncapplications, Proceedings of the Annual Reliability and Maintainability Symposium (RAMS),2003:313-318.
[238] Ramakrishnan A., Pecht M. G., A Life consumption monitoring methodology for electronic systems,IEEE Transactions on Components and Packaging Technologies26(2003)625-634.
[239] Harrison G.F., Smith M. E. F., hurse J., Procedure for aeroengine component life usage prediction,AIDAA/AAAF/DGLR/RAeS5th European Propulsion Forum, Pisa, Italy,1995.
[240] Yan J., Koc M., Lee J., A prognostic algorithm for machine performance assessment and itsapplication, Production Planning and Control76(2004)796-801.
[241] Swanson D.C., A general prognostic tracking algorithm for predictive maintenance, in: IEEEAerospace Conference (2001)2971-2977.
[242] Orchard M.E., Vachtsevanos G. J., A particle-filtering approach for on-line fault diagnosis andfailure prognosis, Transactions of the Institute of Measurement and Control31(4)(2009)221-24.
[243] Tse P. W., Atherton D. P., Prediction of machine deterioration using vibration based fault trends andrecurrent neural networks, Transactions of the ASME: Journal of Vibration and Acoustics121(3)(1999)355-362.
[244] Wang P., Vachtsevanos G., Fault prognostics using dynamic wavelet neural networks, ArtificialIntelligence for Engineering Design, Analysis and Manufacturing15(2001)349-365.
[245] Yang B.S., Widodo A., Support vector machine for machine fault diagnosis and prognosis, Journalof System Design and Dynamics2(1)(2008)12-23.
[246] Wang W. Q., Golnaragh M. F., Ismail F., Prognosis of machine health condition using neuro-fuzzysystems, Mechanical Systems and Signal Processing18(2004)813-831.
[247] Kharoufeh J. P., Cox S. M.(2005). Stochastic models for degradation-based reliability. IIETransactions,37(6),533-542.
[248] Kharoufeh J. P., Solo C., Ulukus M. Y., Semi-Markov models for degradation-based reliability. IIETransactions42(8)(2010)599-612.
[249] Enright M.P., Hudak S.J., McClung R.C., Probabilistic treatment of aircraft engine usage,GT2008-51393, Proceedings of ASME Turbo Expo2008, Power for Land, Sea, and Air,2008,Berlin, Germany.
[250] Research and Technology Organization of NATO. Recommended practices for monitoring gasturbine engine life consumption, Report of the Applied Vehicle Technology Panel Task GroupAVT-017. RTO Technical Report28(RTO-TR-28, AC/323/(AVT)TP/22),2000.
[251] Mathew S., Das D., Osterman M., Pecht M., Ferebee R., Prognostic Assessment of AluminumSupport Structure on a Printed Circuit Board, ASME Journal of Electronic Packaging, Vol.128,No.4, pp.339-345, December2006.
[252] Shetty V., Das D., Pecht M., Hiemstra D., Martin S., Remaining Life Assessment of Shuttle RemoteManipulator System End Effector, Proceedings of the22nd Space Simulation Conference, EllicottCity, MD, October21-23,2002.
[253] Barker J. Gu, D., Pecht M., Prognostics Implementation of Electronics under Vibration Loading,Microelectronics Reliability, Vol.47, No.12, pp.1849-1856, December2007.
[254] Enright M.P., Hudak S.J. Probabilistic mission identification of aircraft engine usage usingnonparametric density estimation techniques, Paper GT2007-27176, Proceedings of the52nd ASMEInternational Gas Turbine&Aeroengine Technical Congress, ASME, Montreal, Canada, May14-17,2007.
[255] Enright M.P., et al.(2006).“Application of probabilistic fracture mechanics to prognosis of aircraftengine components,” AIAA Journal, AIAA,44(2), pp.311-316.
[256] Cox D.R., Oakes D., Analysis of Survival Data, Chapman and Hall:1984.
[257] Kumar, D., B. Klefjo, Proportional Hazards Model: A Review, Reliability Engineering and SystemSafety44,1994:177-188.
[258] Liao H., Zhao W., Guo H., Predicting Remaining Useful Life of an Individual Unit UsingProportional Hazards Model and Logistic Regression Model. Proceedings of the Reliability andMaintainability Symposium (RAMS),2006:127-132.
[259]左洪福,张海军,戎翔.基于比例风险模型的航空发动机视情维修决策[J].航空动力学报,2006(4):716-721.
[260] Coble J., Hines J. W., Identifying Optimal Prognostic Parameters from Data: A Genetic AlgorithmsApproach, Annual Conference of the Prognostics and Health Management Society PHM2009, SanDiego,2009.
[261] Hamada M. S., Using degradation data to assess reliability, Quality Engineering, vol.17, no.4, pp.615-620, Oct.2005.
[262] Suzuki K., Maki K., Yokogawa S.. An analysis of degradation data of a carbon film and propertiesof the estimators. In Statistical Sciences and Data Analysis: Proceedings of the Third Pacific AreaStatistical Conference, K. Matusita, Madan Lal Puri, T. Hayakawa, Ed., Utrecht, Netherlands: BrillAcademic Publishers,1993, pp.501-512.
[263] Bagdonavicius V., Bikelis A., Kazakevi ius V., Statistical analysis of linear degradation and failuretime data with multiple failure modes, Lifetime Data Analysis, vol.10, no.1, pp.65-81, Mar.2004.
[264]任淑红,左洪福,白芳.基于带漂移的布朗运动的民用航空发动机实时性能可靠性预测[J].航空动力学报,2009,24(12):2796-2801.
[265] Pecht M., Radojcic R., Rao G., Guidebook for Managing Silicon Chip Reliability, CRC Press, BocaRaton, FL,1999.
[266] Wang D., Miao Q., Kang R., Robust Health Evaluation of Gearbox Subject to Tooth Failure withWavelet Decomposition, Journal of Sound and Vibration324(3-5)(2009)1141-1157.
[267] Sun J. Z., Cheng S. F., Pecht Michael G.. Prognostics of Multilayer Ceramic Capacitors Via theParameter Residuals. IEEE Transactions on Device and Materials Reliability,2012,12(1):49-57.
[268] Sun J. Z., Zuo H. F., Wang W. B., Michael G. Pecht., Application of a state space modelingtechnique to system prognostics based on a health index for condition-based maintenance.Mechanical Systems and Signal Processing,2012,28:585-596.
[269] Hess, A., The prognostic requirement for advanced sensors and non-traditional detectiontechnologies, DARPA/DSO Prognosis Bidder’s Conference,26-27September, Alexandria, VA,2002.
[270] Virkler D. A., Hillberry B. M., Goel P. K., The statistical nature of fatigue crack propagation, Journalof Engineering Materials Technology,101(1979)148-153.
[271] Goebel, K., Eklund, N., Prognostic Fusion for Uncertainty Reduction. Proceedings of AIAAInfotech@Aerospace Conference. Reston, VA: American Institute for Aeronautics andAstronautics, Inc,2007.
[272] Papazian, J. M., Anagnostou, E. L. J., Engel, S., Fridline, D., Hoitsma, D., Madsen, J., Nardiello J.,Silberstein R. P., Welsh G.., Whiteside J.B., SIPS, A Structural Integrity Prognosis System, IEEEAerospace Conference, March3-10,2007. Big Sky, MT.
[273] Hess A., Calvello G., Frith P., Engel S.J., Hoitsma D., Challenges, Issues, and Lessons LearnedChasing the "Big P": Real Predictive Prognostics Part2, IEEE Aerospace Conference,4-11March2006, Big Sky, MT.
[274] Orchard M., Kacprzynski G., Goebel K., Saha B., Vachtsevanos G., Advances in UncertaintyRepresentation and Management for Particle Filtering Applied to Prognostics, InternationalConference on Prognostics and Health Management, Denver, CO. October6-9,2008.
[275] PHM’08conference: http://www.phmconf.org/.
[276] Gerstsbackh I. B., Kordonskiy K. B., Models of failure[M]. New York: Wiley.1969.
[277] Lu C. J., Meeker W. Q., Using degradation measures to estimate a time-to-failure distribution[J].Technometrics,1993,35(2):161–174.
[278] Crk V., Reliability assessment from degradation data [C]. Proceedings Annual Reliability andMaintainability Symposium, Washington: IEEE,2000:155–161.
[279] Hamada M., Using degradation data to assess reliability [J]. Quality Engineering,2005,17:615-620.
[280]赵建印,刘芳,孙权等.基于性能退化数据的金属化膜电容器可靠性评估[J].电子学报,2005,33(2):378-381.
[281]张永强,冯静,刘琦等.基于Poisson-Normal过程性能退化模型的可靠性分析[J].系统工程与电子技术,2006,28(11):1775-1778.
[282]彭宝华,周经伦,潘正强. Wiener过程性能退化产品可靠性评估的Bayes方法[J].系统工程理论实践,2010,30(3):543-549.
[283]尤琦,赵宇,胡广平等.基于时序模型的加速退化数据可靠性评估[J].系统工程理论实践,2011,31(2):328-332.
[284]钟强晖,张志华,梁胜杰.基于多元退化数据的可靠性分析方法[J].系统工程理论实践,2011,31(3):544-551.
[285] Upadhyaya B.R., Naghedolfeizi M., Raychaudhuri B., Residual Life Estimation of PlantComponents, P/PM Technology June,1994:22-29.
[286] Chinnam R B. On-line reliability estimation for individual components using statistical degradationsignal models [J]. Quality and Reliability Engineering International,2002,18(2):53-73.
[287] Lawless J, Crowder M., Covariates and random effects in a gamma process model with applicationto degradation and failure [J]. Lifetime Data Analysis,2004,10:213–227.
[288]赵建印,刘芳,孙权等.金属化膜脉冲电容器在线可靠性评估与性能预计[J].兵工学报,2006,27(2):266-268.
[289] Harvey A, Koopman S. J., Shephard N., State space and unobserved component models: theory andapplications [M]. Cambridge University Press,2004.
[290] de Freitas N., Rao-Blackwellised particle filtering for fault diagnosis. Proceedings of IEEEAerospace Conference,2002.
[291] Orchard M., Vachtsevanos G., A Particle Filtering Approach for On-Line Fault Diagnosis and FailurePrognosis, Transactions of the Institute of Measurement and Control, vol.31, no.3-4, pp.221-246,June2009.
[292] Yan J., Koc M., Lee J., A prognostic algorithm for machine performance assessment and itsapplication. Production Planning&Control, Vol.15, No.8,2004, pp.796–801.
[293] Doucet A, de Freitas J, Gordon N. Sequential Monte Carlo methods in practice [M]. New York:Springer-Verlag,2001.
[294] Gordon N. J., Salmond D. J., Smith A. F. M., Novel approach to nonlinear/non-Gaussian Bayesianstate estimation [J]. IEE Proceedings on Radar and Signal Processing,1993,140(2):107-113.
[295] Liu J., West M., Combined parameters and state estimation in simulation based filtering [C]. InSequential Monte Carlo Methods in Practice. Springer-Verlag, New York,2001.
[296] Carvalho, C. M., Johannes, M., Lopes, H. F., Polson, N. G., Particle learning and smoothing. Statist.Science,2010,25(1):88-106.
[297] Storvik G. Particle filters in state space models with the presence of unknown static parameters [J].IEEE Transactions on Signal Processing,2002,50(2):281-289.
[298] Polson G. N., Stroud J. R., Muller P., Practical filtering with sequential parameter learning [J].Journal of the Royal Statistical Society: Series B (Statistical Methodology),2008,70(2):413-428.
[299] Andrieu C., Doucet A., Holenstein R., Particle Markov chain Monte Carlo methods [J]. Journal ofthe Royal Statistical Society: Series B (Statistical Methodology),2010,72(3),269–342.
[300] Fearnhead, P.(2008). MCMC for state space models. Technical report, Lancaster University.
[301] Polson G., Stroud J., Muller P., Practical filtering with sequential parameter learning. Journal of theRoyal Statistical Society, Series B (Statistical Methodology)70(2008)413-428.
[302] West M., Harrison J., Bayesian prediction and dynamic models, Springer Series in Statistics.Springer-Verlag, New York,1997.
[303] Zuo M. J., Jiang R., Yam R. C., Approach for reliability modeling of continuous-state devices. IEEETransactions on Reliability,1999,48:9-18.
[304]周长春,民用涡扇发动机超温问题研究及其在使用中的预防[D].西安:西北工业大学,2006.
[305] Jardine A., Lin D., Banjevic D., A review on machinery diagnostics and prognostics implementingcondition based maintenance. Mechanical System Signal Proccessing20(2006)1483-1510.
[306] Saxena A., Goebel K. K., Turbofan Engine Degradation Simulation Data Set. Retrieved from NASAAmes Prognostics Data Repository. http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/,accessed15August2010.
[307] Frederick D., Castro J. De, J. Litt, User’s Guide for the Commercial Modular Aero-PropulsionSystem Simulation (CMAPSS). NASA/ARL,2007.
[308] Saxena A., Goebel K., Simon D., Eklund N., Prognostics Challenge Competition Summary: DamagePropagation Modeling for Aircraft Engine Run‐to‐Failure Simulation, International Conferenceon Prognostics and Health Management,2008: Denver, CO.
[309] Wang T., Yu J., Siegel D., Lee J., A similarity based prognostic approach for remaining useful lifeestimation of engineered systems. in: International conference on prognostics and heathmanagement PHM2008, Denver, USA,2008.
[310] Saxena A., Goebel K., Simon D., Eklund N., Damage propagation modeling for aircraft enginerun-to-failure simulation. in: International conference on prognostics and heath management.PHM2008,2008.
[311] Bannantine J., Comer J. J., Handrock J. L., Fundamentals of Metal Fatigue Analysis, Prentice Hall,New Jersey,1989.
[312] Goebel K., Eklund N., Prognostic Fusion for Uncertainty Reduction, Proceedings ofAIAA@Infotech Aerospace Conference, Reston, VA: American Institute for Aeronautics andAstronautics, Inc,2007.
[313] Cheng S.F., Pecht M., A Fusion Prognostics Method for Remaining Useful Life Prediction ofElectronic Products,5th Annual IEEE Conference on Automation Science and EngineeringBangalore, India, pp.102-107, August22-25,2009.
[314] Maymon G., The Problematic Nature of the Application of Stochastic Crack Growth Models inEngineering Design, Eng. Fract. Mech., Vol.53, No.6,1996, pp.911–916.
[315] Fawaz S., Equivalent Initial Flaw Size Testing and Analysis, Technical ReportAFRL-VA-WP-TR-2000-3024,2000.
[316] Luo J., Bowen P., A probabilistic Methodology for Fatigue Life Prediction, Acta Mater., vol.51, no.12, pp.3537–3550,2003.
[317] Chan K., Enright M., Probabilistic Micromechanical Modeling of Fatigue Life Variability in TiAlloy, Metall. Mater. Trans. A, vol.36, no.10, pp.2621–2631,2005.
[318] Bigerelle M., Najjar D., Fournier B., Rupin N., Iost A., Application of Lambda Distributions andBootstrap Analysis to the Prediction of Fatigue Lifetime and Confidence Intervals, Int. J. Fatigue,vol.28, no.3, pp.223–236,2006.
[319] Ebrahimi N., System Reliability Based on Diffusion Models for Fatigue Crack Growth, NavalRes.Logistics Quart., vol.52, no.1, pp.46–57,2005.
[320] Yang J., Manning S., A Simple Second Order Approximation for Stochastic Crack Growth Analysis,Eng. Fract. Mech., vol.53, no.5, pp.677–686,1996.
[321] Dolinski K., Formulation of a Stochastic Model of Fatigue Crack Growth, Fatigue Fract. Eng.Mater.Struct., vol.16, no.9, pp.1007–1019,1993.
[322] Paris P., Erdogan F., A critical analysis of crack propagation laws, Journal of Basic Engineering,85(1963)528-534.