[1] MEHER-HOMJI C, CHAKER M, MOTIWALLA H. Gas turbine performance deterioration[C]//Proceedings of 30th Turbomachinery Symposium, 2001: 139-176.
[2] HANACHI H, MECHEFSKE C, LIU J, et al. Performance-based gas turbine health monitoring, diagnostics, and prognostics: a survey[J]. IEEE Transactions on Reliability, 2018, 67(3): 1340-1363.
[3] VOLPONI A J. Gas turbine engine health management: past, present, and future trends[J]. Journal of Engineering for Gas Turbines and Power, 2014, 136(5): 051201.
[4] JAW L C. Recent advancements in aircraft engine health management (EHM) technologies and recommendations for the next step[C]//Proceedings of ASME Turbo Expo 2005: Power for Land, Sea, and Air, 2008: 683-695.
[5] LARKIN J, MOAWAD E, PIELUSZCZAK D. Functional aspects of, and trade considerations for, an application-optimized engine health management system (EHMS)[C]//40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. Reston: AIAA, 2004.
[6] HESS A, FILA L. The Joint Strike Fighter (JSF) PHM concept: Potential impact on aging aircraft problems[C]//Proceedings of IEEE Aerospace Conference. Piscataway: IEEE Press, 2002: 6.
[7] FAN Z M, SUN C L, BAI J. Aeroengine fault diagnosis introduction[M]. Beijing: Science Press, 2004 (in Chinese). 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.
[8] TOLANI D, YASAR M, CHIN S, et al. Anomaly detection for health management of aircraft gas turbine engines[C]//Proceedings of the 2005, American Control Conference. Piscataway: IEEE Press, 2005: 459-464.
[9] ASTRUA P, CECCHI S, PIOLA S, et al. Axial compressor degradation effects on heavy duty gas turbines overall performances[C]//Proceedings of ASME Turbo Expo 2013: Turbine Technical Conference and Exposition, 2013.
[10] BONS J P. A review of surface roughness effects in gas turbines[J]. Journal of Turbomachinery, 2010, 132(2): 021004.
[11] ZAITA A V, BULEY G, KARLSONS G. Performance deterioration modeling in aircraft gas turbine engines[J]. Journal of Engineering for Gas Turbines and Power, 1998, 120(2): 344-349.
[12] JAW L C, FRIEND R. ICEMS: a platform for advanced condition-based health management[C]//2001 IEEE Aerospace Conference Proceedings. Piscataway: IEEE Press, 2001: 2909-2914.
[13] DOEL D L. TEMPER-a gas-path analysis tool for commercial jet engines[J]. Journal of Engineering for Gas Turbines and Power, 1994, 116(1): 82-89.
[14] HUANG J Q, WANG Q H, LU F. Research status and prospect of gas path fault diagnosis for aeroengine[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2020, 52(4): 507-522 (in Chinese). 黄金泉, 王启航, 鲁峰. 航空发动机气路故障诊断研究现状与展望[J]. 南京航空航天大学学报, 2020, 52(4): 507-522.
[15] VOLPONI A J, TANG L. Improved engine health monitoring using full flight data and companion engine information[J]. SAE International Journal of Aerospace, 2016, 9(1): 91-102.
[16] ZHOU X, LU F, HUANG J Q. Fault diagnosis based on measurement reconstruction of HPT exit pressure for turbofan engine[J]. Chinese Journal of Aeronautics, 2019, 32(5): 1156-1170.
[17] CHANG X D, HUANG J Q, LU F. Health parameter estimation with second-order sliding mode observer for a turbofan engine[J]. Energies, 2017, 10(7): 1040.
[18] CHANG X D, HUANG J Q, LU F. Robust in-flight sensor fault diagnostics for aircraft engine based on sliding mode observers[J]. Sensors (Basel, Switzerland), 2017, 17(4): 835.
[19] LU F, ZHENG W H, HUANG J Q, et al. Life cycle performance estimation and in-flight health monitoring for gas turbine engine[J]. Journal of Dynamic Systems, Measurement, and Control, 2016, 138(9): 091009.
[20] BROTHERTON T, VOLPONI A L, LUPPOLD R, et al. eSTORM: Enhanced self tuning on-board real-time engine model[C]//2003 IEEE Aerospace Conference Proceedings. Piscataway: IEEE Press, 2003: 3075-3086.
[21] BAI J, FAN Z, SUN C. Consistence criterion for engine fault diagnosis decision[C]//Proceedings of the Third Asian-Pacific Conference on Aerospace Technology and Science, 2000: 407-413.
[22] DAVISON C R, BIRK A M. Development of fault diagnosis and failure prediction techniques for small gas turbine engines[C]//Proceedings of ASME Turbo Expo 2001: Power for Land, Sea, and Air, 2014.
[23] BORGUET S, DEWALLEF P, LE'ONARD O. A way to deal with model-plant mismatch for a reliable diagnosis in transient operation[C]//Proceedings of ASME Turbo Expo 2006: Power for Land, Sea, and Air, 2008: 593-602.
[24] DEWALLEF P, LE'ONARD O. On-line performance monitoring and engine diagnostic using robust Kalman filtering techniques[C]//Proceedings of ASME Turbo Expo 2003, Collocated With the 2003 International Joint Power Generation Conference, 2009: 395-403.
[25] WANG Q H, HUANG J Q, LU F. An improved particle filtering algorithm for aircraft engine gas-path fault diagnosis[J]. Advances in Mechanical Engineering, 2016, 8(7): 168781401665960.
[26] YOU C X, HUANG J Q, LU F. Recursive reduced kernel based extreme learning machine for aero-engine fault pattern recognition[J]. Neurocomputing, 2016, 214: 1038-1045.
[27] ZHOU H W, HUANG J Q, LU F, et al. Echo state kernel recursive least squares algorithm for machine condition prediction[J]. Mechanical Systems and Signal Processing, 2018, 111: 68-86.
[28] ZHOU H W, HUANG J Q, LU F. Reduced kernel recursive least squares algorithm for aero-engine degradation prediction[J]. Mechanical Systems and Signal Processing, 2017, 95: 446-467.
[29] ZEDDA M, SINGH R. Fault diagnosis of a turbofan engine using neural networks-A quantitative approach[C]//34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. Reston: AIAA, 1998.
[30] PU X X, LIU S M, JIANG H D, et al. Sparse Bayesian learning for gas path diagnostics[J]. Journal of Engineering for Gas Turbines and Power, 2013, 135(7): 071601.
[31] LU F, JIANG J P, HUANG J Q, et al. Dual reduced kernel extreme learning machine for aero-engine fault diagnosis[J]. Aerospace Science and Technology, 2017, 71: 742-750.
[32] LU J J, HUANG J Q, LU F. Kernel extreme learning machine with iterative picking scheme for failure diagnosis of a turbofan engine[J]. Aerospace Science and Technology, 2020, 96: 105539.
[33] LU F, LI Z H, HUANG J Q, et al. Hybrid state estimation for aircraft engine anomaly detection and fault accommodation[J]. AIAA Journal, 2020, 58(4): 1748-1762.
[34] LU F. Aeroengine fault diagnostics based on fusion technique[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2009 (in Chinese). 鲁峰. 航空发动机故障诊断的融合技术研究[D]. 南京: 南京航空航天大学, 2009.
[35] MCFADDEN P D, SMITH J D. Vibration monitoring of rolling element bearings by the high-frequency resonance technique—A review[J]. Tribology International, 1984, 17(1): 3-10.
[36] LIANG M, SOLTANI BOZCHALOOI I. An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection[J]. Mechanical Systems and Signal Processing, 2010, 24(5): 1473-1494.
[37] ANTONI J. Cyclic spectral analysis of rolling-element bearing signals: facts and fictions[J]. Journal of Sound and Vibration, 2007, 304(3-5): 497-529.
[38] DYBAŁA J, ZIMROZ R. Rolling bearing diagnosing method based on Empirical Mode Decomposition of machine vibration signal[J]. Applied Acoustics, 2014, 77: 195-203.
[39] ANTONI J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1): 108-124.
[40] LUAN X C, SHA Y D. Technology to extract weak fault characteristic signal of intermediate bearing of some turbofan engine[J]. Science Technology and Engineering, 2018, 18(13): 167-174 (in Chinese). 栾孝驰, 沙云东. 某型涡扇发动机中介轴承微弱故障特征信号提取技术[J]. 科学技术与工程, 2018, 18(13): 167-174.
[41] YUAN Y, ZHAO X, FEI J Y, et al. Study on fault diagnosis of rolling bearing based on time-frequency generalized dimension[J]. Shock and Vibration, 2015, 2015: 808457.
[42] SHANG B L, XIE Z L, CHENG L, et al. Early fault diagnosis of complex rotor systems by EMD-ICA[J]. Science Technology and Engineering, 2014, 14(2): 265-271 (in Chinese). 尚柏林, 谢紫龙, 程礼, 等. EMD与ICA相结合的复杂转子系统早期故障诊断[J]. 科学技术与工程, 2014, 14(2): 265-271.
[43] ZHANG Y. Fault diagnosis of rollingbearing's early weak fault based on resonance sparse decomposition[J]. Chinese Journal of Construction Machinery, 2017, 15(2): 182-188 (in Chinese). 张勇. 基于共振稀疏分解的滚动轴承早期微弱故障诊断[J]. 中国工程机械学报, 2017, 15(2): 182-188.
[44] WIGGINS R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16(1-2): 21-35.
[45] SAWALHI N, RANDALL R B, ENDO H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis[J]. Mechanical Systems and Signal Processing, 2007, 21(6): 2616-2633.
[46] WANG H C, CHEN J, DONG G M. Fault diagnosis method for rolling bearing's weak fault based on minimum entropy deconvolution and sparse decomposition[J]. Journal of Mechanical Engineering, 2013, 49(1): 88-94 (in Chinese). 王宏超, 陈进, 董广明. 基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取[J]. 机械工程学报, 2013, 49(1): 88-94.
[47] TANG G J, LIU S K. Incipient fault diagnosis method for rolling bearing based on VMD and spectral kurtosis[J]. China Measurement & Test, 2017, 43(9): 112-117 (in Chinese). 唐贵基, 刘尚坤. 基于VMD和谱峭度的滚动轴承早期故障诊断方法[J]. 中国测试, 2017, 43(9): 112-117.
[48] LIU S K, TANG G J, WANG X L. Incipient fault diagnosis method for rolling bearing based on MED and variational mode decomposition[J]. Journal of Mechanical Transmission, 2017, 41(9): 179-182 (in Chinese). 刘尚坤, 唐贵基, 王晓龙. 基于MED和变分模态分解的滚动轴承早期故障诊断方法[J]. 机械传动, 2017, 41(9): 179-182.
[49] MCDONALD G L, ZHAO Q, ZUO M J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems and Signal Processing, 2012, 33: 237-255.
[50] WANG C G, PANG Z, REN X P, et al. Early fault diagnosis of roller bearing based on ensemble local mean decomposition and maximum correlated kurtosis deconvolution[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(11): 1764-1770 (in Chinese). 王朝阁, 庞震, 任学平, 等. ELMD和MCKD在滚动轴承早期故障诊断中的应用[J]. 机械科学与技术, 2017, 36(11): 1764-1770.
[51] Editorial board forAeroengine Design Manual. Aeroengine design manual: Vol. 12[M]. Beijing: Aviation Industry Press, 2002 (in Chinese). 《航空发动机设计手册》总编委会. 航空发动机设计手册: 第12册[M]. 北京: 航空工业出版社, 2002.
[52] YANG Y. Failure analysis of aero-engine accessory gearbox[D]. Chongqing: Chongqing University, 2016 (in Chinese). 杨语. 航空发动机附件机匣传动齿轮失效分析研究[D]. 重庆: 重庆大学, 2016.
[53] CHEN L M, LI W T, WANG G, et al. Fracture failure analysis of driving bevel gear in main reducer nut in an aero-engine[C]//The 15th Annual Meeting of China Association for Science and Technology, the 13th Session: Proceedings of Aero-engine Design, Manufacturing and Application Technology Workshop, 2013: 484-488 (in Chinese). 陈立民, 李文天, 汪刚, 等. 某型发动机附件机匣主动锥齿轮断裂失效分析[C]//第十五届中国科协年会第13分会场: 航空发动机设计、制造与应用技术研讨会论文集, 2013: 484-488.
[54] SHI Y Y. Research on some reliability problems of accessory casing based on thermal analysis[D]. Shenyang: Northeastern University, 2009 (in Chinese). 史妍妍. 基于热分析的附件机匣若干可靠性问题研究[D]. 沈阳: 东北大学, 2009.
[55] MCFADDEN P D. Detecting fatigue cracks in gears by amplitude and phase demodulation of the meshing vibration[J]. Journal of Vibration and Acoustics, 1986, 108(2): 165-170.
[56] MCFADDEN P D. A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox[J]. Journal of Sound and Vibration, 1991, 144(1): 163-172.
[57] MCFADDEN P D, SMITH J D. An explanation for the asymmetry of the modulation sidebands about the tooth meshing frequency in epicyclic gear vibration[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 1985, 199(1): 65-70.
[58] MCFADDEN P D. Examination of a technique for the early detection of failure in gears by signal processing of the time domain average of the meshing vibration[J]. Mechanical Systems and Signal Processing, 1987, 1(2): 173-183.
[59] LEI Y G, LIN J, ZUO M J, et al. Condition monitoring and fault diagnosis of planetary gearboxes: A review[J]. Measurement, 2014, 48: 292-305.
[60] SHARMA V, PAREY A. A review of gear fault diagnosis using various condition indicators[J]. Procedia Engineering, 2016, 144: 253-263.
[61] WANG D, TSUI K L, MIAO Q. Prognostics and health management: A review of vibration based bearing and gear health indicators[J]. IEEE Access, 2018, 6: 665-676.
[62] XUE S. An investigation of gear meshing behavior of planetary gear systems for improved fault diagnosis[D]. Perth: Curtin University, 2016.
[63] CHEN X W, FENG Z P. Time-frequency analysis of torsional vibration signals in resonance region for planetary gearbox fault diagnosis under variable speed conditions[J]. IEEE Access, 2017, 5: 21918-21926.
[64] SINOU J J, LEES A W. A non-linear study of a cracked rotor[J]. European Journal of Mechanics-A/Solids, 2007, 26(1): 152-170.
[65] PATEL T H, DARPE A K. Influence of crack breathing model on nonlinear dynamics of a cracked rotor[J]. Journal of Sound and Vibration, 2008, 311(3-5): 953-972.
[66] SINOU J J, LEES A W. The influence of cracks in rotating shafts[J]. Journal of Sound and Vibration, 2005, 285(4-5): 1015-1037.
[67] MENG G, HAHN E J. Dynamic response of a cracked rotor with some comments on crack detection[J]. Journal of Engineering for Gas Turbines and Power, 1997, 119(2): 447-455.
[68] ZHU H J, ZHAO M, WANG D Y. A study on the dynamics of a cracked Jeffcott rotor[J]. Journal of Vibration and Shock, 2001, 20(1): 1-4 (in Chinese). 朱厚军, 赵玫, 王德洋. Jeffcott裂纹转子动力特性的研究[J]. 振动与冲击, 2001, 20(1): 1-4.
[69] YANG Y F, REN X M, QIN W Y. Nonlinear response prediction of cracked rotor based on EMD[C]//54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston: AIAA, 2013.
[70] SABNAVIS G, KIRK R G, KASARDA M, et al. Cracked shaft detection and diagnostics: A literature review[J]. The Shock and Vibration Digest, 2004, 36 (4): 287-296.
[71] WANG Y F, ZHU J, TENG G R, et al. 1(1/2)-dimension spectrum analysis on early cracked fault characters of aero engine rotors[J]. Journal of Vibration and Shock, 2015, 34(1): 88-93, 134 (in Chinese). 王艳丰, 朱靖, 滕光蓉, 等. 航空发动机转子早期裂纹故障振动特征的1(1/2)维谱分析[J]. 振动与冲击, 2015, 34(1): 88-93, 134.
[72] DARPE A K, GUPTA K, CHAWLA A. Coupled bending, longitudinal and torsional vibrations of a cracked rotor[J]. Journal of Sound and Vibration, 2004, 269(1-2): 33-60.
[73] NELSON H D, NATARAJ C. The dynamics of a rotor system with a cracked shaft[J]. Journal of Vibration, Acoustics, Stress and Reliability in Design, 1986, 108(2): 189-196.
[74] TIAN W G, PAN M C, LUO F L, et al. Design of special eddy current transducer for crack in an aeroengine labyrinth disc[J]. Chinese Journal of Sensors and Actuators, 2008, 21(7): 1151-1154 (in Chinese). 田武刚, 潘孟春, 罗飞路, 等. 用于某型航空发动机篦齿盘裂纹检测的专用涡流传感器设计[J]. 传感技术学报, 2008, 21(7): 1151-1154.
[75] TIAN W G, PAN M C, CHEN L X. In situ nondestructive testing for an aeroengine labyrinth disc[J]. Computer Measurement & Control, 2012, 20(8): 2031-2033 (in Chinese). 田武刚, 潘孟春, 陈棣湘. 航空发动机篦齿盘的原位无损检测[J]. 计算机测量与控制, 2012, 20(8): 2031-2033.
[76] FU G Q, ZHENG Y, JING P, et al. Development of endoscope and eddy current integral testing instrument and application on an aeroengine[J]. Nondestructive Testing Technologying, 2010, 32(2): 134-137 (in Chinese). 付刚强, 郑勇, 景鹏, 等. 内窥涡流一体化综合检测仪研制及在某航空发动机上的应用[J]. 无损检测, 2010, 32 (2): 134-137.
[77] DING X P, LI Z, WANG C, et al. The flaw detection method of the tenon tooth of the turbine disc[C]//Proceedings of the 9th Shaanxi NDT Annual Meeting, 2004: 141, 163-168 (in Chinese). 丁晓萍, 李泽, 王婵, 等. 涡轮盘榫齿探伤方法[C]//陕西省第九届无损检测年会论文集, 2004: 141, 163-168.
[78] WOIKE M, ABDUL-AZIZ A, CLEM M, et al. Optical strain and crack-detection measurements on a rotating disk[C]//Smart Sensor Phenomena, Technology, Networks, and Systems Integration, 2013, 8693: 173-188.
[79] HU, B, YU R Q, XU W J. Micro-magnetic NDT for surface crack defect in a GH4169 turbine disc simulated by artificial groove[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(10): 3450-3456 (in Chinese). 胡博, 于润桥, 徐伟津. 人工槽模拟GH4169涡轮盘表面裂纹缺陷的微磁检测[J]. 航空学报, 2015, 36(10): 3450-3456.
[80] WEI G S, QI G J. Crack detection on dovetail gear of certain type airplane engine turbine disk[J]. Nondestructive Testing Technologying, 2009, 31(6): 497-498, 507 (in Chinese). 魏桂生, 齐共金. 飞机发动机涡轮盘榫齿裂纹的探伤[J]. 无损检测, 2009, 31(6): 497-498, 507.
[81] YU X, ZHANG W M, QIU Z C, et al. Differentialexcitation eddy current sensor testing for aircraft engine blades defect[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(9): 1582-1588 (in Chinese). 于霞, 张卫民, 邱忠超, 等. 飞机发动机叶片缺陷的差激励涡流传感器检测[J]. 北京航空航天大学学报, 2015, 41(9): 1582-1588.
[82] DONG L M, LI J, NI C Y, et al. Crack detection of engine blade based on laser-heating assisted surface acoustic waves generated by scanning laser[J]. Chinese Journal of Lasers, 2011, 38(11): 1103001 (in Chinese). 董利明, 李加, 倪辰荫, 等. 基于光热调制检测发动机叶片疲劳裂纹的激光声表面波方法[J]. 中国激光, 2011, 38(11): 1103001.
[83] DONG R Q, HE X, LIU Y A. The in-situ ultrasonic inspection of cracks in deep blade root[J]. Nondestructive Testing Technologying, 2016, 38(1): 38-40 (in Chinese). 董瑞琴, 何喜, 刘永安. 叶片深根裂纹的原位超声波检测[J]. 无损检测, 2016, 38(1): 38-40.
[84] ZENZINGER G, BAMBERG J, DUMM M, et al. Crack detection using EddyTherm[C]//AIP Conference Proceedings, 2005, 760 (1): 1646-1653.
[85] MA Y H, CAO C, HAO Y, et al. Structure and dynamics analysis of rotor system in geared turbofan engine[J]. Journal of Aerospace Power, 2015, 30(11): 2753-2761 (in Chinese). 马艳红, 曹冲, 郝勇, 等. 齿轮传动风扇发动机转子系统结构与动力学分析[J]. 航空动力学报, 2015, 30(11): 2753-2761.
[86] YAN W Z, LIAO X, CAO C, et al. Structure and dynamics analysis of low pressure rotor in geared turbofan[J]. Journal of Aerospace Power, 2015, 30(12): 2863-2869 (in Chinese). 颜文忠, 廖鑫, 曹冲, 等. 齿轮传动涡扇发动机低压转子结构与动力学分析[J]. 航空动力学报, 2015, 30(12): 2863-2869.
[87] MARSH G. Aero engines lose weight thanks to composites[J]. Reinforced Plastics, 2012, 56(6): 32-35.
[88] CAREY B. Southwest engine failure ripples: Reminiscent of 2016 inflight engine failure; carrier accelerates ultrasonic fan blade inspection[J]. Aviation Week & Space Technology, 2018: 1-27.
[89] WOIKE M, ABDUL-AZIZ A, OZA N, et al. New sensors and techniques for the structural health monitoring of propulsion systems[J]. The Scientific World Journal, 2013, 2013: 596506.
[90] GIURGIUTIU V, SOUTIS C. Enhanced composites integrity through structural health monitoring[J]. Applied Composite Materials, 2012, 19(5): 813-829.
[91] RULLI R P, DOTTA F. Developments towards the qualification of two SHM systems for S-SHM application[C]//Structural Health Monitoring, 2015.
[92] GHOSHAL A. Sensor applications for structural diagnostics and prognostics[C]//Proceedings of the International Symposium on Engineering under Uncertainty: Safety Assessment and Management (ISEUSAM-2012), 2013: 503-516.
[93] GARCIA I, BELOKI J, ZUBIA, J, et al. An optical fiber bundle sensor for tip clearance and tip timing measurements in a turbine rig[J]. Sensors (Basel, Switzerland), 2013, 13(6): 7385-7398.
[94] LIU P P, ZUO H F, FU Y, et al. Study of on-line monitoring and diagnosis of aero-engine rubbing fault[J]. Chinese Journal of Scientific Instrument, 2013, 34(7): 164-169 (in Chinese). 刘鹏鹏, 左洪福, 付宇, 等. 航空发动机碰摩故障在线监测与诊断研究[J]. 仪器仪表学报, 2013, 34(7): 164-169.
[95] LIN J M, LI H L, ZHAO J H. Study on dynamic monitoring technology of aero-engine blades[C]//The 9th International Symposium on NDT in Aerospace, 2017.
[96] ZHANG X W. Application of metal additive manufacturing in aero-engine[J]. Journal of Aerospace Power, 2016, 31(1): 10-16 (in Chinese). 张小伟. 金属增材制造技术在航空发动机领域的应用[J]. 航空动力学报, 2016, 31(1): 10-16.
[97] YAN X, RUAN X Q. Application and development of additive manufacturing technology in aeroengine[J]. Aeronautical Manufacturing Technology, 2016, 59(21): 70-75 (in Chinese). 闫雪, 阮雪茜. 增材制造技术在航空发动机中的应用及发展[J]. 航空制造技术, 2016, 59(21): 70-75.
[98] WANG Q, SUN Y. Applications of additive manufacturing technology on aero-engine[J]. Aeronautical Science & Technology, 2014, 25(9): 6-10 (in Chinese). 王强, 孙跃. 增材制造技术在航空发动机中的应用[J]. 航空科学技术, 2014, 25(9): 6-10.
[99] LI G Q. Present and future of aeroengine oil system[J]. Aeroengine, 2011, 37(6): 49-52, 62 (in Chinese). 李国权. 航空发动机滑油系统的现状及未来发展[J]. 航空发动机, 2011, 37(6): 49-52, 62.
[100] ZHU J, HE D, BECHHOEFER E. A survey of lubrication oil condition monitoring, diagnostics, and prognostics techniques and systems[J]. Journal of Chemical Science and Technology, 2013, 2(3): 100-115.
[101] SHINDE H, BEWOOR A. Analyzing the relationship between the deterioration of engine oil in terms of change in viscosity, conductivity and transmittance[C]//2017 International Conference on Advances in Mechanical, Industrial, Automation and Management Systems (AMIAMS). Piscataway: IEEE Press, 2017: 36-41.
[102] SHINDE H, BEWOOR A. Capacitive sensor for engine oil deterioration measurement[C]//American Institute of Physics Conference Series, 2018: 020099.
[103] BALASHANMUGAM V, GOBALAKICHENIN D. Development of dielectric sensor to monitor the engine lubricating oil degradation[J]. Thermal Science, 2016, 20(Suppl. 4): 1061-1069.
[104] GONG L H, XU Y J, HAN S S, et al. Research on fault monitoring and diagnosis system of multi-agents and network based aircraft engine lubrication system[J]. Lubrication Engineering, 2003, 28(1): 25-26, 43 (in Chinese). 龚烈航, 徐延军, 韩寿松, 等. 基于多Agent和网络的航空发动机滑油系统故障监测与诊断系统研究[J]. 润滑与密封, 2003, 28(1): 25-26, 43.
[105] ZHU H Q, MA X Y, ZHANG Y G, et al. Research on aero engine oil fault diagnosis expert system[J]. Microcomputer Information, 2008, 24(34): 150-152 (in Chinese). 朱焕勤, 马晓宇, 张永国, 等. 航空发动机油液故障诊断专家系统研究[J]. 微计算机信息, 2008, 24(34): 150-152.
[106] WANG L G. Research on fault diagnosis expert system of aviation engine's lubricating oil system[J]. Science & Technology Ecnony Market, 2010(9): 9-11 (in Chinese). 王立纲. 航空发动机的滑油系统故障诊断专家系统研究[J]. 科技经济市场, 2010(9): 9-11.
[107] HOU S L, WANG W, HU J H, et al. Fault diagnosis of aeroengine lubricating system based on genetic programming[J]. Journal of Vibration, Measurement & Diagnosis, 2008, 28(4): 400-403, 416 (in Chinese). 侯胜利, 王威, 胡金海, 等. 基于遗传编程的发动机滑油系统故障诊断[J]. 振动、测试与诊断, 2008, 28 (4): 400-403, 416.
[108] WANG D, YANG J F, JIAO Z. Research on fault diagnosis of aviation-engine lubricant system based on wavelet neural networks[J]. Instrumentation Technology, 2008(11): 10-12 (in Chinese). 王东, 杨军锋, 焦准. 基于小波网络航空发动机滑油系统故障诊断方法研究[J]. 仪表技术, 2008(11): 10-12.
[109] JIAO Z, YANG D W. Research on aviation-engine lubricant system fault diagnosis based on wavelet neural networks[J]. Aviation Maintenance & Engineering, 2009(1): 58-60 (in Chinese). 焦准, 杨笃伟. 基于小波网络航空发动机滑油系统故障诊断方法研究[J]. 航空维修与工程, 2009(1): 58-60.
[110] ZHANG R, XIE W J. Research on aeroengine lubricant system fault diagnosis based on wavelet neural network[J]. Aeronautical Manufacturing Technology, 2009, 52(6): 85-89 (in Chinese). 张蓉, 谢武杰. 基于小波神经网络航空发动机滑油系统故障诊断方法研究[J]. 航空制造技术, 2009, 52(6): 85-89.
[111] REN Z C. Research on application of aero-engine condition monitoring in lubricating oil system of test bench[J]. Science and Technology Innovation Herald, 2012, 9(10): 81 (in Chinese). 任忠朝. 航空发动机状态监控在试车台滑油系统上的应用研究[J]. 科技创新导报, 2012, 9(10): 81.
[112] ZHANG D F, DUANMU J S, CAO Y Q, et al. Fusion diagnosis of engine lub oil system based on integrated intelligent algorithm[J]. Measurement & Control Technology, 2013, 32(10): 44-47, 51 (in Chinese). 张东峰, 端木京顺, 曹轶乾, 等. 基于集成智能算法的发动机滑油系统融合诊断[J]. 测控技术, 2013, 32(10): 44-47, 51.
[113] XU W, CHENG G, CHEN Y T, et al. Method for fault diagnosis of Bayesian network inference in marine lube oil system[J]. Journal of Sichuan Ordnance, 2015, 36(3): 86-90 (in Chinese). 许伟, 程刚, 陈于涛, 等. 舰船柴油主机滑油系统贝叶斯网络推理故障诊断方法[J]. 四川兵工学报, 2015, 36 (3): 86-90.
[114] WANG X G, LIU Z G, ZHENG Y, et al. Application of statistical theory in fault diagnosis of aero-engine lubricating oil system[J]. Plant Maintenance Engineering, 2016 (11): 114-117 (in Chinese). 王晓钢, 刘振岗, 郑宇, 等. 统计诊断理论用于航空发动机滑油系统故障诊断[J]. 设备管理与维修, 2016 (11): 114-117.
[115] CHEN N T, MA T, WANG J, et al. Aviation engine oil leakage fault analysis based on fuzzy fault tree theory[J]. Computer Measurement & Control, 2016, 24 (6): 64-67 (in Chinese). 陈农田, 马婷, 王杰, 等. 模糊故障树分析法在航空发动机滑油渗漏分析中的应用[J]. 计算机测量与控制, 2016, 24(6): 64-67.
[116] CHEN K J, CHEN Y Y, WU X W. Application of grey incidence fault tree analysis in aero-engine lubricant system[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2017, 39(4): 386-390 (in Chinese). 陈可嘉, 陈媛媛, 吴兴旺. 灰色关联故障树在航空发动机滑油系统的应用[J]. 武汉理工大学学报(信息与管理工程版), 2017, 39(4): 386-390.
[117] JIA G F. Application of fuzzy tree in fault diagnosis of marine diesel engine lubricating oil system[J]. Hebei Agricultural Machinery, 2020(5): 83-84, 109 (in Chinese). 贾广付. 模糊故障树在船舶柴油机滑油系统故障诊断中的应用[J]. 河北农机, 2020(5): 83-84, 109.
[118] YU X, ZHAO Y, GENG J W. Knowledge acquisition of expert diagnosis of a oil system based on extended fault tree[J]. Science & Technology Information, 2014, 12(10): 14 (in Chinese). 于鑫, 赵勇, 耿建伟. 基于扩展故障树的某滑油系统专家诊断知识获取[J]. 科技资讯, 2014, 12(10): 14.
[119] LIANG M Z, ZHOU D J, ZHANG H S, et al. Gas turbine lubricating oil system fault diagnosis based on improved D-S evidence theory[J]. Gas Turbine Technology, 2018, 31(2): 17-22, 36 (in Chinese). 梁茂宗, 周登极, 张会生, 等. 基于改进D-S证据理论的燃气轮机滑油系统故障诊断[J]. 燃气轮机技术, 2018, 31(2): 17-22, 36.
[120] GUO P Y, QIN L S, YU Q, et al. Discussion on No. 3 bearing fault of CF56-3 engine[J]. Aviation Engineering & Maintenance, 2001(6): 18-20 (in Chinese). 郭盼优, 秦理锁, 于强, 等. CFM56-3发动机3号轴承故障探讨[J]. 航空工程与维修, 2001(6): 18-20.
[121] WAN J. Cause analysis of metal chip failure ofLycoming engine[J]. Journal of Flying College Caac, 2005, 16(6): 62-64 (in Chinese). 万军. 莱康明发动机金属屑故障成因分析[J]. 中国民航飞行学院学报, 2005, 16(6): 62-64.
[122] ZHANG J, ZHU Z X, CHEN D, et al. Applications of atomic emission spectrometric techniques on wear failure analysis of aircraft engine[J]. Failure Analysis and Prevention, 2007, 2(2): 62-64, 33 (in Chinese). 张晶, 朱子新, 陈栋, 等. 原子发射光谱技术在航空发动机磨损失效分析中的应用[J]. 失效分析与预防, 2007, 2(2): 62-64, 33.
[123] SUN H G, HUO W J, YU H B, et al. Monitoring and diagnosis technology of aero engine oil system[C]//2000 Annual Meeting of Combustion, Heat and Mass Transfer Committee of Power Branch of Chinese Aeronautical Society, 2000: 23-24, 38 (in Chinese). 孙护国, 霍武军, 于海滨, 等. 航空发动机滑油系统监控与诊断技术[C]//中国航空学会动力专业分会燃烧与传热传质专业委员会学术年会, 2000: 23-24, 38.
[124] YAO H Y. Application of lubricant chip analysis on aero-engine condition monitoring[J]. Failure Analysis and Prevention, 2006, 1(3): 60-63 (in Chinese). 姚红宇. 滑油中金属屑分析在航空发动机状态监控中的应用[J]. 失效分析与预防, 2006, 1(3): 60-63.
[125] ZHU Z X, CHEN D, ZHANG J, et al. Large metal debris monitoring techniques for aircraft engines[J]. Aviation Maintenance & Engineering, 2006(3): 30-32 (in Chinese). 朱子新, 陈栋, 张晶, 等. 航空发动机大颗粒金属磨屑监控技术[J]. 航空维修与工程, 2006(3): 30-32.
[126] SAE. SAE AIR1828B: Guide engine lubrication system monitoring[S]. Warrendale: SAE International, 1984.
[127] SAE. AIR1828C: Guide to engine lubrication system monitoring: SAE AIR 1828: 2014[S]. Warrendale: SAE International, 2014.
[128] TOMS A, JORDAN E, HUMPHREY G. The success of filter debris analysis for J52 engine condition based maintenance[C]//41 st AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit. Reston: AIAA, 2005.
[129] SUN Y S, YANG H, TONG H B, et al. Review of on-line detection for wear particles in lubricating oil of aviation engine[J]. Chinese Journal of Scientific Instrument, 2017, 38(7): 1561-1569 (in Chinese). 孙衍山, 杨昊, 佟海滨, 等. 航空发动机滑油磨粒在线监测[J]. 仪器仪表学报, 2017, 38(7): 1561-1569.
[130] LIN K, ZHAO J P, HAO J. Aeroengine lubricating oil metal chip detection system[J]. Information & Communications, 2017, 30(3): 81-82 (in Chinese). 林凯, 赵建平, 郝建. 一种航空发动机滑油金属屑检测系统[J]. 信息通信, 2017, 30(3): 81-82.
[131] SUN L P, CHEN G, CHEN L B, et al. Image recognition of aero-engine oil filter debris by kernel principle component analysis[J]. Mechanical Science and Technology for Aerospace Engineering, 2010, 29(6): 731-736 (in Chinese). 孙丽萍, 陈果, 陈立波, 等. 基于KPCA的航空发动机滑油滤磨屑图像识别[J]. 机械科学与技术, 2010, 29(6): 731-736.
[132] CHEN L B, CHEN G, SONG K, et al. Image-based quantitative analysis technique of aero-engine filter debris[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(2): 368-376 (in Chinese). 陈立波, 陈果, 宋科, 等. 基于图像的发动机滑油滤磨屑定量分析技术[J]. 航空学报, 2011, 32(2): 368-376.
[133] CHEN G, SONG L Q, CHEN L B. Knowledge acquisition for aero-engine wear fault diagnosis based on rule extraction from neural networks[J]. Journal of Aerospace Power, 2008, 23(12): 2170-2176 (in Chinese). 陈果, 宋兰琪, 陈立波. 基于神经网络规则提取的航空发动机磨损故障诊断知识获取[J]. 航空动力学报, 2008, 23(12): 2170-2176.
[134] CHEN L B, SONG L Q, CHEN G. Study on fusion diagnosis techniques of wear faults in synthesized monitoring of aero-engine[J]. Journal of Aerospace Power, 2009, 24(1): 169-175 (in Chinese). 陈立波, 宋兰琪, 陈果. 航空发动机滑油综合监控中的磨损故障融合诊断研究[J]. 航空动力学报, 2009, 24(1): 169-175.
[135] SHANG W, WANG Y S, ZHANG M J, et al. Oil metal particles detection algorithm based on wavelet transform[J]. MATEC Web of Conferences, 2017, 100: 02001.
[136] ZHANG J, LI Y J, CAO Y Y, et al. Immune SVM used in wear fault diagnosis of aircraft engine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(7): 1419-1425 (in Chinese). 张建, 李艳军, 曹愈远, 等. 免疫支持向量机用于航空发动机磨损故障诊断[J]. 北京航空航天大学学报, 2017, 43(7): 1419-1425.
[137] CHEN Y G, ZENG L C, LV M J. Fault simulation of the hydraulic position servo system based on AMESim[J]. Machine Tool & Hydraulics, 2007, 35(9): 215-216, 219 (in Chinese). 陈阳国, 曾良才, 吕敏建. 基于AMESim的液压位置伺服系统故障仿真[J]. 机床与液压, 2007, 35(9): 215-216, 219.
[138] ZHANG X Y, CHEN X H, HE Q F. Modeling and simulation for fault of hydraulic actuator based on AMESim[J]. Hydraulics Pneumatics & Seals, 2011, 31(10): 26-28, 48 (in Chinese). 张宪宇, 陈小虎, 何庆飞. 基于AMESim的液压缸故障建模与仿真[J]. 液压气动与密封, 2011, 31(10): 26-28, 48.
[139] YU L, YE Z F, WANG B. Simulation and experimental study of characteristics for aeroengine fuel metering device[J]. Aeroengine, 2015, 41(2): 85-88 (in Chinese). 余玲, 叶志锋, 王彬. 航空发动机燃油计量装置特性仿真与试验研究[J]. 航空发动机, 2015, 41(2): 85-88.
[140] GAO J H, ZHANG J, BIAN B H, et al. Simulation on lubrication system via AMESim[J]. Chinese Journal of Construction Machinery, 2016, 14(2): 137-141 (in Chinese). 高久好, 张靖, 卞斌华, 等. 基于AMESim的某润滑系统仿真[J]. 中国工程机械学报, 2016, 14(2): 137-141.
[141] CHEN X Z, XIA Z J, WANG L J. Application ofAMESim simulation analysis method in fuel regulator troubleshooting[J]. Aeroengine, 2019, 45(1): 46-50 (in Chinese). 陈新中, 夏宗记, 王玲君. AMESim仿真分析方法在燃油调节器排故中的应用[J]. 航空发动机, 2019, 45(1): 46-50.
[142] BAI J, ZHU Y X, HE W B, et al. Fault simulation of a certain type of aeroengine lubricating oil supply system based on AMESim[J]. Science Technology and Engineering, 2020, 20(9): 3784-3789 (in Chinese). 白杰, 朱永新, 何文博, 等. 基于AMESim的某型航空发动机滑油供油系统故障模拟[J]. 科学技术与工程, 2020, 20(9): 3784-3789.
[143] FU Q. Main fuel control system of aeroengine based on mode reference sliding mode control[J]. Chinese Hydraulics & Pneumatics, 2013(2): 77-79 (in Chinese). 傅强. 航空发动机主燃油机械液压控制系统仿真研究[J]. 液压与气动, 2013(2): 77-79.
[144] ZHANG L, FU J Y, LIU H Y. Simulation and analysis of servomachanism with feedback failure[J]. Chinese Hydraulics & Pneumatics, 2014(5): 122-125 (in Chinese). 张亮, 傅俊勇, 刘洪宇. 伺服机构反馈故障仿真与分析[J]. 液压与气动, 2014(5): 122-125.
[145] GAO Z H, MA C B, SONG D. Aircraft fuel feeding system performance degradation and failure prediction[J]. Journal of Northwestern Polytechnical University, 2015, 33(2): 209-215 (in Chinese). 高泽海, 马存宝, 宋东. 飞机燃油供油系统性能退化与故障预测[J]. 西北工业大学学报, 2015, 33(2): 209-215.
[146] YAN X H, GUO Y Q, YIN K, et al. Modeling simulation and optimization of oil system based on MATLAB/Simulink[J]. Journal of Aerospace Power, 2017, 32(3): 740-748 (in Chinese). 闫星辉, 郭迎清, 殷锴, 等. 基于MATLAB/Simulink的滑油系统建模仿真与优化[J]. 航空动力学报, 2017, 32(3): 740-748.
[147] DONG J. Simulationstudy on fault monitoring of hydraulic servo actuator based on model comparison[J]. Metrology & Measurement Technology, 2019, 39(5): 20-28 (in Chinese). 董骥. 基于模型比较的液压伺服作动器故障监控仿真研究[J]. 计测技术, 2019, 39(5): 20-28.
[148] YANG Y M, LU Q S. The research of dynamic modeling and analysis of commercial engine fuel-metering unit[J]. ManufacturingAutomation, 2016, 38(6): 106-110, 130 (in Chinese)
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