基于热力模型的燃气轮机气路故障预测诊断研究综述
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摘要
为避免失修和过修,提高设备的可靠性和可用性,降低燃气轮机机组运维成本,用户宜采用预知维修策略。气路诊断是一种对正在演变或即将发生的恶化情况发布早期预警信息的有效技术。经过多年发展,气路诊断已经取得了许多基于燃气轮机(准)稳态的诊断算法理论成果,但尚未形成一个完整的科学体系。现今燃气轮机越来越需在电网支持模式下更灵活地运行(包括频繁变工况及瞬态加减载)。在此操作条件下,其使用寿命会比在基本负荷(准)稳态运行时消耗更快,因此,迫切需要考虑瞬态变工况下气路故障诊断与预测的新理论新方法。针对瞬态变工况下气路故障预测诊断的基础问题,提出了瞬态变工况下燃气轮机自适应气路故障预测诊断方法研究路线,包括研究可变几何部件及瞬态变工况运行模式的自适应动态热力建模方法;利用机组可测气路参数,研究基于部件特性线形状内在非线性自适应的气路诊断方法,实时诊断得到各主要部件健康参数;最后提出一种融合各主要部件健康参数的多维度时序预测方法,为实现复杂强非线性热力系统故障诊断与预测提出新方法。
To avoid disrepair and over-repair, improve equipment reliability and availability while maximizing service life and reduce O&M costs, users should adopt predictive maintenance strategies. Gas-path diagnosis is an effective technical means of disseminating early warning information for evolving or impending deterioration. After years of development, many diagnosis methods based on gas turbine steady state/quasi-steady state were obtained, but no complete scientific system was formed yet. Today's gas turbines are increasingly required to operate more flexibly in grid support modes(including frequent change conditions and transient loading/unloading operating modes). Under such operating conditions, the service life of the gas turbine will be consumed faster than that during base load steady-state/quasi-steady-state operation. Therefore, it is urgent to consider new theories and new methods of gas-path diagnosis under transient conditions. This project studied the fundamental problems in the gas-path fault predictive diagnosis under transient conditions. First, an adaptive dynamic performance modeling method with variable geometry component was proposed. Using the measurable gas-path parameters of the unit, the gas-path diagnosis method based on the intrinsic nonlinear adaptation of the shape of the component characteristic maps was studied, to obtain the main components health parameters real-time. Finally, a multi-dimensional time series prediction method was proposed based on the fusion of the diagnostic information of the main components.
To avoid disrepair and over-repair, improve equipment reliability and availability while maximizing service life and reduce O&M costs, users should adopt predictive maintenance strategies. Gas-path diagnosis is an effective technical means of disseminating early warning information for evolving or impending deterioration. After years of development, many diagnosis methods based on gas turbine steady state/quasi-steady state were obtained, but no complete scientific system was formed yet. Today's gas turbines are increasingly required to operate more flexibly in grid support modes(including frequent change conditions and transient loading/unloading operating modes). Under such operating conditions, the service life of the gas turbine will be consumed faster than that during base load steady-state/quasi-steady-state operation. Therefore, it is urgent to consider new theories and new methods of gas-path diagnosis under transient conditions. This project studied the fundamental problems in the gas-path fault predictive diagnosis under transient conditions. First, an adaptive dynamic performance modeling method with variable geometry component was proposed. Using the measurable gas-path parameters of the unit, the gas-path diagnosis method based on the intrinsic nonlinear adaptation of the shape of the component characteristic maps was studied, to obtain the main components health parameters real-time. Finally, a multi-dimensional time series prediction method was proposed based on the fusion of the diagnostic information of the main components.
引文
[1]杨承,丁苏,任洪运,等.天然气燃料预混水蒸气的燃气轮机低氮燃烧研究[J].中国电机工程学报,2018,38(4):1111-1118.Yang Cheng,Ding Su,Ren Hongyun,et al.Study on De NOx combustion of gas turbine by natural gas premixed with steam[J].Proceedings of the CSEE,2018,38(4):1111-1118(in Chinese).
[2]邢畅,邱朋华,刘栗,等.起动机驱动过程中微型燃气轮机燃烧室变工况燃烧特性[J].中国电机工程学报,2018,38(8):2367-2375,2546.Xing Chang,Qiu Penghua,Liu Li,et al.Off-design combustion performance under starter-driven process for a micro-gas turbine combustor[J].Proceedings of the CSEE,2018,38(8):2367-2375,2546(in Chinese).
[3]杨承,黄曼曼,王平,等.压气机抽气储能改进燃气轮机能源系统灵活性分析[J].中国电机工程学报,2018,38(18):5510-5517.Yang Cheng,Huang Manman,Wang Ping,et al.Analysis on improving flexibility of a gas turbine-based energy system with compressor bypass air extraction for energy storage[J].Proceedings of the CSEE,2018,38(18):5510-5517(in Chinese).
[4]Tahan M,Tsoutsanis E,Muhammad M,et al.Performance-based health monitoring,diagnostics and prognostics for condition-based maintenance of gas turbines:a review[J].Applied Energy,2017,198:122-144.
[5]蒋东翔,刘超,杨文广,等.关于重型燃气轮机预测诊断与健康管理的研究综述[J].热能动力工程,2015,30(2):173-179.Jiang Dongxiang,Liu Chao,Yang Wenguang,et al.Overview concerning the study of the prediction,diagnosis and health management(PHM)of heavy-duty gas turbines[J].Journal of Engineering for Thermal Energy and Power,2015,30(2):173-179(in Chinese).
[6]Ying Yulong,Li Jingchao,Chen Zhimin,et al.Study on rolling bearing on-line reliability analysis based on vibration information processing[J].Computers&Electrical Engineering,2018,69:842-851.
[7]Li Yiguang.Gas turbine performance and health status estimation using adaptive gas path analysis[J].Journal of Engineering for Gas Turbines and Power,2010,132(4):041701.
[8]Tsoutsanis E,Meskin N,Benammar M,et al.Dynamic performance simulation of an aeroderivative gas turbine using the matlab simulink environment[C]//Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition.San Diego,California,USA:ASME,2013:15-21.
[9]Gülen S C,Kim K.Gas turbine combined cycle dynamic simulation:a physics based simple approach[J].Journal of Engineering for Gas Turbines and Power,136(1):1-15.
[10]杨承,黄志峰,马晓茜.联合循环热电联产机组变工况性能分析[J].中国电机工程学报,2017,37(12):3514-3524.Yang Cheng,Huang Zhifeng,Ma Xiaoqian.Study on off-design characteristics of combined heat and power based on combined cycle gas turbine units[J].Proceedings of the CSEE,2017,37(12):3514-3524(in Chinese).
[11]Tsoutsanis E,Meskin N,Benammar M,et al.A dynamic prognosis scheme for flexible operation of gas turbines[J].Applied Energy,2016,164:686-701.
[12]Capata R.An artificial neural network-based diagnostic methodology for gas turbine path analysis(part I):introduction[J].Energy,Ecology and Environment,2016,1(6):343-350.
[13]Kestner B K,Lee Y K,Voleti G,et al.Diagnostics of highly degraded industrial gas turbines using Bayesian networks[C]//Proceedings of the ASME 2011 Turbo Expo:Turbine Technical Conference and Exposition.Vancouver,British Columbia,Canada:ASME,2011:39-49.
[14]Lee Y K,Mavris D N,Volovoi V V,et al.A fault diagnosis method for industrial gas turbines using Bayesian data analysis[J].Journal of Engineering for Gas Turbines and Power,2010,132(4):041602.
[15]Salar A,Sedigh A K,Hosseini S,et al.A hybrid EKF-fuzzy approach to fault detection and isolation of industrial gas turbines[C]//Proceedings of the ASME 2011Turbo Expo:Turbine Technical Conference and Exposition.Vancouver,British Columbia,Canada:ASME,2011:251-260.
[16]Kong C,Lim S.Study on fault diagnostics of a turboprop engine using inverse performance model and artificial intelligent methods[C]//Proceedings of the ASME 2011Turbo Expo:Turbine Technical Conference and Exposition.Vancouver,British Columbia,Canada:ASME,2011:75-83.
[17]Wang L,Li Yiguang,Ghafir M F A.Rough set diagnostic frameworks for gas turbine fault classification[C]//Proceedings of the ASME Turbo Expo 2013:Turbine Technical Conference and Exposition.San Antonio,Texas,USA:ASME,2013:V002T07A007.
[18]徐鹏,曹云鹏,欧惠宇,等.船用三轴燃气轮机气路故障建模与聚类诊断技术[J].中国舰船研究,2014,9(3):89-92.Xu Peng,Cao Yunpeng,Ou Huiyu,et al.Gas path fault modeling and cluster diagnosis for three-shaft marine gas turbines[J].Chinese Journal of Ship Research,2014,9(3):89-92(in Chinese).
[19]赵雄飞,刘永葆,贺星,等.基于小偏差方法的燃气轮机气路故障判据的建立[J].机械工程与自动化,2011(4):115-117.Zhao Xiongfei,Liu Yongbao,He Xing,et al.Creation of gas path fault criterion based on small deviation method for gas turbine[J].Mechanical Engineering&Automation,2011(4):115-117(in Chinese).
[20]鲁峰,黄金泉,吕怡秋,等.基于非线性自适应滤波的发动机气路部件健康诊断方法[J].航空学报,2013,34(11):2529-2538.Lu Feng,Huang Jinquan,Lv Yiqiu,et al.Aircraft engine gas-path components health diagnosis based on nonlinear adaptive filters[J].Acta Aeronautica et Astronautica Sinica,2013,34(11):2529-2538(in Chinese).
[21]Li Yiguang.Training future engineers on gas turbine gas path diagnostics using PYTHIA[C]//Proceedings of the ASME Turbo Expo 2014:Turbine Technical Conference and Exposition.Düsseldorf,Germany:ASME,2014:V006T08A003.
[22]Fentaye A D,Gilani S I U H,Baheta A T.Gas turbine gas path diagnostics:a review[J].MATEC Web of Conferences,2016,74:00005.
[23]Tsoutsanis E,Meskin N,Benammar M,et al.Transient gas turbine performance diagnostics through nonlinear adaptation of compressor and turbine maps[J].Journal of Engineering for Gas Turbines and Power,2015,137(9):091201.
[24]Tsoutsanis E,Meskin N,Benammar M,et al.Acomponent map tuning method for performance prediction and diagnostics of gas turbine compressors[J].Applied Energy,2014,135:572-585.
[25]Li Yiguang,Ghafir M F A,Wang L,et al.Nonlinear multiple points gas turbine off-design performance adaptation using a genetic Algorithm[J].Journal of Engineering for Gas Turbines and Power,2011,133(7):071701.
[26]Li Yiguang,Ghafir M F A,Wang L,et al.Improved multiple point nonlinear genetic algorithm based performance adaptation using least square method[J].Journal of Engineering for Gas Turbines and Power,2012,134(3):031701.
[27]Tsoutsanis E,Li Yiguang,Pilidis P,et al.Part-load performance of gas turbines:part II-multi-point adaptation with compressor map generation and GA optimization[C]//Proceedings of the ASME 2012 Gas Turbine India Conference.Mumbai,Maharashtra,India:ASME,2012:743-751.
[28]Tsoutsanis E,Meskin N.Derivative-driven window-based regression method for gas turbine performance prognostics[J].Energy,2017,128:302-311.
[29]Tsoutsanis E,Li Yiguang,Pilidis P,et al.Non-linear model calibration for off-design performance prediction of gas turbines with experimental data[J].The Aeronautical Journal,2017,121(1245):1758-1777.
[30]Lee J J,Kang D W,Kim T S.Development of a gas turbine performance analysis program and its application[J].Energy,2011,36(8):5274-5285.
[31]Zhang Guoqiang,Zheng Jiongzhi,Xie Angjun,et al.Thermodynamic analysis of combined cycle under design/off-design conditions for its efficient design and operation[J].Energy Conversion and Management,2016,126:76-88.
[32]Aldi N,Morini M,Pinelli M,et al.Cross validation of multistage compressor map generation by means of computational fluid dynamics and stage-stacking techniques[C]//Proceedings of the ASME Turbo Expo2014:Turbine Technical Conference and Exposition.Düsseldorf,Germany:ASME,2014:V03BT25A009.
[33]Ying Yulong,Cao Yunpeng,Li Shuying,et al.Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis[J].Advances in Mechanical Engineering,2016,8(1):1-14.
[34]邢媛.基于离散工作点集扩维的燃气轮机故障诊断方法研究[D].哈尔滨:哈尔滨工业大学,2013.Xing Yuan.Method for gas turbine fault diagnosis based on discrete operating point sets[D].Harbin:Harbin Institute of Technology,2013(in Chinese).
[35]Dewallef P,Borguet S.A methodology to improve the robustness of gas turbine engine performance monitoring against sensor faults[J].Journal of Engineering for Gas Turbines and Power,2013,135(5):051601.
[36]Larsson E,?slund J,Frisk E,et al.Gas turbine modeling for diagnosis and control[J].Journal of Engineering for Gas Turbines and Power,2014,136(7):071601.
[37]冯敏.涡扇发动机气路健康参数滤波估计方法研究[D].南京:南京航空航天大学,2013.Feng Min.Gas path health parameters filtering estimation for turbo-fan engine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2013(in Chinese).
[38]Panov V.Auto-tuning of real-time dynamic gas turbine models[C]//Proceedings of the ASME Turbo Expo 2014Turbine Technical Conference and Exposition.Düsseldorf,Germany:ASME,2014:16-20.
[39]Ying Yulong,Cao Yunpeng,Li Shuying,et al.Nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm[J].Mathematical Problems in Engineering,2015,2015:940757.
[40]Li Jingchao,Ying Yulong.A method to improve the robustness of gas turbine gas-path fault diagnosis against sensor faults[J].IEEE Transactions on Reliability,2018,67(1):3-12.
[41]Chen Min,Hu Liangquan,Tang Hailong.An approach for optimal measurements selection on gas turbine engine fault diagnosis[J].Journal of Engineering for Gas Turbines and Power,2015,137(7):071203.
[42]胡良权,陈敏,唐海龙,等.双轴分排涡扇发动机气路故障诊断测量参数选择方法[J].航空动力学报,2015,30(8):1853-1861.Hu Liangquan,Chen Min,Tang Hailong,et al.Measurement parameters selection method for gas path fault diagnosis of two-shaft split flow turbofan engine[J].Journal of Aerospace Power,2015,30(8):1853-1861(in Chinese).
[43]Zaidan M A,Relan R,Mills A R,et al.Prognostics of gas turbine engine:an integrated approach[J].Expert Systems with Applications,2015,42(22):8472-8483.
[44]Zhao Ningbo,Yang Jialong Li Shuying,et al.A GM(1,1)Markov chain-based aeroengine performance degradation forecast approach using exhaust gas temperature[J].Mathematical Problems in Engineering,2014,2014:832851.
[45]赵玉婷.民航发动机在翼寿命预测模型方法研究[D].南京:南京航空航天大学,2010.Zhao Yuting.Research on methods of life on wing prediction for civil aeroengine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2010(in Chinese).
[2]邢畅,邱朋华,刘栗,等.起动机驱动过程中微型燃气轮机燃烧室变工况燃烧特性[J].中国电机工程学报,2018,38(8):2367-2375,2546.Xing Chang,Qiu Penghua,Liu Li,et al.Off-design combustion performance under starter-driven process for a micro-gas turbine combustor[J].Proceedings of the CSEE,2018,38(8):2367-2375,2546(in Chinese).
[3]杨承,黄曼曼,王平,等.压气机抽气储能改进燃气轮机能源系统灵活性分析[J].中国电机工程学报,2018,38(18):5510-5517.Yang Cheng,Huang Manman,Wang Ping,et al.Analysis on improving flexibility of a gas turbine-based energy system with compressor bypass air extraction for energy storage[J].Proceedings of the CSEE,2018,38(18):5510-5517(in Chinese).
[4]Tahan M,Tsoutsanis E,Muhammad M,et al.Performance-based health monitoring,diagnostics and prognostics for condition-based maintenance of gas turbines:a review[J].Applied Energy,2017,198:122-144.
[5]蒋东翔,刘超,杨文广,等.关于重型燃气轮机预测诊断与健康管理的研究综述[J].热能动力工程,2015,30(2):173-179.Jiang Dongxiang,Liu Chao,Yang Wenguang,et al.Overview concerning the study of the prediction,diagnosis and health management(PHM)of heavy-duty gas turbines[J].Journal of Engineering for Thermal Energy and Power,2015,30(2):173-179(in Chinese).
[6]Ying Yulong,Li Jingchao,Chen Zhimin,et al.Study on rolling bearing on-line reliability analysis based on vibration information processing[J].Computers&Electrical Engineering,2018,69:842-851.
[7]Li Yiguang.Gas turbine performance and health status estimation using adaptive gas path analysis[J].Journal of Engineering for Gas Turbines and Power,2010,132(4):041701.
[8]Tsoutsanis E,Meskin N,Benammar M,et al.Dynamic performance simulation of an aeroderivative gas turbine using the matlab simulink environment[C]//Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition.San Diego,California,USA:ASME,2013:15-21.
[9]Gülen S C,Kim K.Gas turbine combined cycle dynamic simulation:a physics based simple approach[J].Journal of Engineering for Gas Turbines and Power,136(1):1-15.
[10]杨承,黄志峰,马晓茜.联合循环热电联产机组变工况性能分析[J].中国电机工程学报,2017,37(12):3514-3524.Yang Cheng,Huang Zhifeng,Ma Xiaoqian.Study on off-design characteristics of combined heat and power based on combined cycle gas turbine units[J].Proceedings of the CSEE,2017,37(12):3514-3524(in Chinese).
[11]Tsoutsanis E,Meskin N,Benammar M,et al.A dynamic prognosis scheme for flexible operation of gas turbines[J].Applied Energy,2016,164:686-701.
[12]Capata R.An artificial neural network-based diagnostic methodology for gas turbine path analysis(part I):introduction[J].Energy,Ecology and Environment,2016,1(6):343-350.
[13]Kestner B K,Lee Y K,Voleti G,et al.Diagnostics of highly degraded industrial gas turbines using Bayesian networks[C]//Proceedings of the ASME 2011 Turbo Expo:Turbine Technical Conference and Exposition.Vancouver,British Columbia,Canada:ASME,2011:39-49.
[14]Lee Y K,Mavris D N,Volovoi V V,et al.A fault diagnosis method for industrial gas turbines using Bayesian data analysis[J].Journal of Engineering for Gas Turbines and Power,2010,132(4):041602.
[15]Salar A,Sedigh A K,Hosseini S,et al.A hybrid EKF-fuzzy approach to fault detection and isolation of industrial gas turbines[C]//Proceedings of the ASME 2011Turbo Expo:Turbine Technical Conference and Exposition.Vancouver,British Columbia,Canada:ASME,2011:251-260.
[16]Kong C,Lim S.Study on fault diagnostics of a turboprop engine using inverse performance model and artificial intelligent methods[C]//Proceedings of the ASME 2011Turbo Expo:Turbine Technical Conference and Exposition.Vancouver,British Columbia,Canada:ASME,2011:75-83.
[17]Wang L,Li Yiguang,Ghafir M F A.Rough set diagnostic frameworks for gas turbine fault classification[C]//Proceedings of the ASME Turbo Expo 2013:Turbine Technical Conference and Exposition.San Antonio,Texas,USA:ASME,2013:V002T07A007.
[18]徐鹏,曹云鹏,欧惠宇,等.船用三轴燃气轮机气路故障建模与聚类诊断技术[J].中国舰船研究,2014,9(3):89-92.Xu Peng,Cao Yunpeng,Ou Huiyu,et al.Gas path fault modeling and cluster diagnosis for three-shaft marine gas turbines[J].Chinese Journal of Ship Research,2014,9(3):89-92(in Chinese).
[19]赵雄飞,刘永葆,贺星,等.基于小偏差方法的燃气轮机气路故障判据的建立[J].机械工程与自动化,2011(4):115-117.Zhao Xiongfei,Liu Yongbao,He Xing,et al.Creation of gas path fault criterion based on small deviation method for gas turbine[J].Mechanical Engineering&Automation,2011(4):115-117(in Chinese).
[20]鲁峰,黄金泉,吕怡秋,等.基于非线性自适应滤波的发动机气路部件健康诊断方法[J].航空学报,2013,34(11):2529-2538.Lu Feng,Huang Jinquan,Lv Yiqiu,et al.Aircraft engine gas-path components health diagnosis based on nonlinear adaptive filters[J].Acta Aeronautica et Astronautica Sinica,2013,34(11):2529-2538(in Chinese).
[21]Li Yiguang.Training future engineers on gas turbine gas path diagnostics using PYTHIA[C]//Proceedings of the ASME Turbo Expo 2014:Turbine Technical Conference and Exposition.Düsseldorf,Germany:ASME,2014:V006T08A003.
[22]Fentaye A D,Gilani S I U H,Baheta A T.Gas turbine gas path diagnostics:a review[J].MATEC Web of Conferences,2016,74:00005.
[23]Tsoutsanis E,Meskin N,Benammar M,et al.Transient gas turbine performance diagnostics through nonlinear adaptation of compressor and turbine maps[J].Journal of Engineering for Gas Turbines and Power,2015,137(9):091201.
[24]Tsoutsanis E,Meskin N,Benammar M,et al.Acomponent map tuning method for performance prediction and diagnostics of gas turbine compressors[J].Applied Energy,2014,135:572-585.
[25]Li Yiguang,Ghafir M F A,Wang L,et al.Nonlinear multiple points gas turbine off-design performance adaptation using a genetic Algorithm[J].Journal of Engineering for Gas Turbines and Power,2011,133(7):071701.
[26]Li Yiguang,Ghafir M F A,Wang L,et al.Improved multiple point nonlinear genetic algorithm based performance adaptation using least square method[J].Journal of Engineering for Gas Turbines and Power,2012,134(3):031701.
[27]Tsoutsanis E,Li Yiguang,Pilidis P,et al.Part-load performance of gas turbines:part II-multi-point adaptation with compressor map generation and GA optimization[C]//Proceedings of the ASME 2012 Gas Turbine India Conference.Mumbai,Maharashtra,India:ASME,2012:743-751.
[28]Tsoutsanis E,Meskin N.Derivative-driven window-based regression method for gas turbine performance prognostics[J].Energy,2017,128:302-311.
[29]Tsoutsanis E,Li Yiguang,Pilidis P,et al.Non-linear model calibration for off-design performance prediction of gas turbines with experimental data[J].The Aeronautical Journal,2017,121(1245):1758-1777.
[30]Lee J J,Kang D W,Kim T S.Development of a gas turbine performance analysis program and its application[J].Energy,2011,36(8):5274-5285.
[31]Zhang Guoqiang,Zheng Jiongzhi,Xie Angjun,et al.Thermodynamic analysis of combined cycle under design/off-design conditions for its efficient design and operation[J].Energy Conversion and Management,2016,126:76-88.
[32]Aldi N,Morini M,Pinelli M,et al.Cross validation of multistage compressor map generation by means of computational fluid dynamics and stage-stacking techniques[C]//Proceedings of the ASME Turbo Expo2014:Turbine Technical Conference and Exposition.Düsseldorf,Germany:ASME,2014:V03BT25A009.
[33]Ying Yulong,Cao Yunpeng,Li Shuying,et al.Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis[J].Advances in Mechanical Engineering,2016,8(1):1-14.
[34]邢媛.基于离散工作点集扩维的燃气轮机故障诊断方法研究[D].哈尔滨:哈尔滨工业大学,2013.Xing Yuan.Method for gas turbine fault diagnosis based on discrete operating point sets[D].Harbin:Harbin Institute of Technology,2013(in Chinese).
[35]Dewallef P,Borguet S.A methodology to improve the robustness of gas turbine engine performance monitoring against sensor faults[J].Journal of Engineering for Gas Turbines and Power,2013,135(5):051601.
[36]Larsson E,?slund J,Frisk E,et al.Gas turbine modeling for diagnosis and control[J].Journal of Engineering for Gas Turbines and Power,2014,136(7):071601.
[37]冯敏.涡扇发动机气路健康参数滤波估计方法研究[D].南京:南京航空航天大学,2013.Feng Min.Gas path health parameters filtering estimation for turbo-fan engine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2013(in Chinese).
[38]Panov V.Auto-tuning of real-time dynamic gas turbine models[C]//Proceedings of the ASME Turbo Expo 2014Turbine Technical Conference and Exposition.Düsseldorf,Germany:ASME,2014:16-20.
[39]Ying Yulong,Cao Yunpeng,Li Shuying,et al.Nonlinear steady-state model based gas turbine health status estimation approach with improved particle swarm optimization algorithm[J].Mathematical Problems in Engineering,2015,2015:940757.
[40]Li Jingchao,Ying Yulong.A method to improve the robustness of gas turbine gas-path fault diagnosis against sensor faults[J].IEEE Transactions on Reliability,2018,67(1):3-12.
[41]Chen Min,Hu Liangquan,Tang Hailong.An approach for optimal measurements selection on gas turbine engine fault diagnosis[J].Journal of Engineering for Gas Turbines and Power,2015,137(7):071203.
[42]胡良权,陈敏,唐海龙,等.双轴分排涡扇发动机气路故障诊断测量参数选择方法[J].航空动力学报,2015,30(8):1853-1861.Hu Liangquan,Chen Min,Tang Hailong,et al.Measurement parameters selection method for gas path fault diagnosis of two-shaft split flow turbofan engine[J].Journal of Aerospace Power,2015,30(8):1853-1861(in Chinese).
[43]Zaidan M A,Relan R,Mills A R,et al.Prognostics of gas turbine engine:an integrated approach[J].Expert Systems with Applications,2015,42(22):8472-8483.
[44]Zhao Ningbo,Yang Jialong Li Shuying,et al.A GM(1,1)Markov chain-based aeroengine performance degradation forecast approach using exhaust gas temperature[J].Mathematical Problems in Engineering,2014,2014:832851.
[45]赵玉婷.民航发动机在翼寿命预测模型方法研究[D].南京:南京航空航天大学,2010.Zhao Yuting.Research on methods of life on wing prediction for civil aeroengine[D].Nanjing:Nanjing University of Aeronautics and Astronautics,2010(in Chinese).