ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (9): 625574-625574.doi: 10.7527/S1000-6893.2021.25574
• Special Topic: Operation Safety of Aero-engine • Previous Articles Next Articles
CAO Ming1,2, WANG Peng1,2, ZUO Hongfu3, ZENG Haijun1, SUN Jianzhong3, YANG Weidong4, WEI Fang1, CHEN Xuefeng5
CJA
Received:2021-03-26
Revised:2021-05-11
Online:2022-09-15
Published:2021-08-25
Supported by:CLC Number:
CAO Ming, WANG Peng, ZUO Hongfu, ZENG Haijun, SUN Jianzhong, YANG Weidong, WEI Fang, CHEN Xuefeng. Current status, challenges and opportunities of civil aero-engine diagnostics & health management Ⅱ: Comprehensive off-board diagnosis, life management and intelligent condition based MRO[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(9): 625574-625574.
| [1] 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. [2] RONG X, ZUO H F, CHEN Z X. Civil aero-engine health management integrating with life prediction and maintenance decision-making[C]//2010 Prognostics and System Health Management Conference. Piscataway: IEEE Press, 2010: 1-6. [3] MARZAT J, PIET-LAHANIER H, DAMONGEOT F, et al. Model-based fault diagnosis for aerospace systems: a survey[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2012, 226(10): 1329-1360. [4] HALSMER R, MATSON R. Smoothing CFM56 engine removal rate at USAir[C]//28th Joint Propulsion Conference and Exhibit. Reston: AIAA, 1992. [5] STRANJAK A, DUTTA P S, EBDEN M, et al. A multi-agent simulation system for prediction and scheduling of aero engine overhaul[C]//Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems: Industrial Track. New York: ACM, 2008: 81-88. [6] AERDATA. IT systems to manage & optimize engine maintenance[J]. Maintenance & Engineering, 2010(76): 45-51. [7] PETERSON T J, SUNDARESHAN M K. Information value mapping for fusion architectures[C]//Proceedings of the Third International Conference on Information Fusion. Piscataway: IEEE Press, 2000. [8] NIU Y M, WONG Y S, HONG G S. An intelligent sensor system approach for reliable tool flank wear recognition[J]. The International Journal of Advanced Manufacturing Technology, 1998, 14(2): 77-84. [9] LIU M H, XIAO D Y. Method of multisensor data classification based on entropy[J]. Control and Decision, 2006, 21(4): 410-414(in Chinese). 刘敏华, 萧德云. 基于信息熵的多传感器数据分类方法[J]. 控制与决策, 2006, 21(4): 410-414. [10] HE Y, WANG G H, LU D J. Multisensor information fusion with applications[M]. 2nd ed. Beijing: Publishing House of Electronics Industry, 2007 (in Chinese). 何友, 王国宏, 陆大. 多传感器信息融合及应用[M]. 2版. 北京: 电子工业出版社, 2007. [11] ZHU X J, CHEN X. An information fusion method of altitude sensors for UAV[J]. Acta Aeronautica et Astronautica Sinica, 2008, 29(S1): 61-65 (in Chinese). 朱晓娟, 陈欣. 一种无人机高度传感器信息融合方法[J]. 航空学报, 2008, 29(S1): 61-65. [12] MADANI K, CHEBIRA A, BOUCHEFRA K, et al. Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance[J]. Optical Engineering, 1998, 37(2): 370-377. [13] WANG J Y, WANG G R. Decision fusion of redundant knowledge based on rough set[J]. Control and Decision, 2007, 22(6): 657-662 (in Chinese). 王加阳, 王国仁. 基于粗集的多知识库决策融合[J]. 控制与决策, 2007, 22(6): 657-662. [14] CAO X H, LIU H W, WU S J. Utilization of multiple polarization data and multiple features for radar target identification[J]. Systems Engineering and Electronics, 2008, 30(2): 261-264 (in Chinese). 曹向海, 刘宏伟, 吴顺君. 多极化多特征融合的雷达目标识别研究[J]. 系统工程与电子技术, 2008, 30(2): 261-264. [15] WANG J G, LUO J Q. Passive tracking based on data association with information fusion of multi-feature and multi-target[J]. Acta Electronica Sinica, 2004, 32(6): 1013-1016 (in Chinese). 王杰贵, 罗景青. 基于多目标多特征信息融合数据关联的无源跟踪方法[J]. 电子学报, 2004, 32(6): 1013-1016. [16] WANG Y Z, LIANG Y, ZHAO C H, et al. Kernel-based tracking based on adaptive fusion of multiple cues[J]. Acta Automatica Sinica, 2008, 34(4): 393-399 (in Chinese). 王永忠, 梁彦, 赵春晖, 等. 基于多特征自适应融合的核跟踪方法[J]. 自动化学报, 2008, 34(4): 393-399. [17] VOLPONI A. Data fusion for enhanced aircraft engine prognostics and health management: NASA/CR-2005-214055[R]. Washington, D.C. : NASA, 2005. [18] DEMPSEY P J, KREIDER G, FICHTER T. Investigation of tapered roller bearing damage detection using oil debris analysis[C]//2006 IEEE Aerospace Conference. Piscataway: IEEE Press, 2006. [19] CHEN G, YANG Y W, ZUO H F. Intelligent fusion for aeroengine wear fault diagnosis[J]. Transactions of Nanjing University of Aeronautics & Astronautics, 2006, 23(4): 297-303. [20] CHEN T, SUN J G, HAO Y. Neural network and dempster-shafter theory based fault diagnosis for aeroengine gas path[J]. Acta Aeronautica et Astronautica Sinica, 2006, 27(6): 1014-1017 (in Chinese). 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, 2006, 27(6): 1014-1017. [21] CAI K L, XIE S S, WU Y. Study on fuzzy fault diagnosis of aero-engines[J]. Journal of Aerospace Power, 2007, 22(5): 833-837 (in Chinese). 蔡开龙, 谢寿生, 吴勇. 航空发动机的模糊故障诊断方法研究[J]. 航空动力学报, 2007, 22(5): 833-837. [22] LU F, HUANG J Q, QIU X J, et al. Feature extraction based on information entropy fusion for turbo-shaft engine gas-path analysis[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 13-19 (in Chinese). 鲁峰, 黄金泉, 仇小杰, 等. 基于信息熵融合提取特征的发动机气路分析[J]. 仪器仪表学报, 2012, 33(1): 13-19. [23] SUN J Z, ZUO H F. Gas path analysis based on multi-sources diagnostics information fusion[J]. Journal of Aerospace Power, 2013, 28(8): 1885-1896 (in Chinese). 孙见忠, 左洪福. 基于多源诊断信息融合的发动机气路分析[J]. 航空动力学报, 2013, 28(8): 1885-1896. [24] LI Y B, LI Q H, HUANG X H, et al. Research on gas fault fusion diagnosis of aero-engine component[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(6): 1612-1622 (in Chinese). 李业波, 李秋红, 黄向华, 等. 航空发动机气路部件故障融合诊断方法研究[J]. 航空学报, 2014, 35(6): 1612-1622. [25] RENGASAMY D, MORVAN H P, FIGUEREDO G P. Deep learning approaches to aircraft maintenance, repair and overhaul: a review[C]//2018 21st International Conference on Intelligent Transportation Systems (ITSC). Piscataway: IEEE Press, 2018: 150-156. [26] MEMARZADEH M, MATTHEWS B, TEMPLIN T. Multiclass anomaly detection in flight data using semi-supervised explainable deep learning model[J]. Journal of Aerospace Information Systems, 2021, 19(2): 83-97. [27] BLEU-LAINE M H, PURANIK T G, MAVRIS D N, et al. Predicting adverse events and their precursors in aviation using multi-class multiple-instance learning[C]//AIAA Scitech 2021 Forum. Reston: AIAA, 2021. [28] SINA TAYARANI-BATHAIE S, KHORASANI K. Fault detection and isolation of gas turbine engines using a bank of neural networks[J]. Journal of Process Control, 2015, 36: 22-41. [29] AMOZEGAR M, KHORASANI K. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines[J]. Neural Networks, 2016, 76: 106-121. [30] HE Q, LI N, LUO W J, et al. A survey of machine learning algorithms for big data[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(4): 327-336 (in Chinese). 何清, 李宁, 罗文娟, 等. 大数据下的机器学习算法综述[J]. 模式识别与人工智能, 2014, 27(4): 327-336. [31] ZHANG R, LI W P, MO T. Review of deep learning[J]. Information and Control, 2018, 47(4): 385-397, 410 (in Chinese). 张荣, 李伟平, 莫同. 深度学习研究综述[J]. 信息与控制, 2018, 47(4): 385-397, 410. [32] WEN C L, LV F Y. Review on deep learning based fault diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(1): 234-248 (in Chinese). 文成林, 吕菲亚. 基于深度学习的故障诊断方法综述[J]. 电子与信息学报, 2020, 42(1): 234-248. [33] SHEN T, LI S M, XIN Y. Review on fault diagnosis of rotating machinery based on deep learning[J]. Computer Measurement & Control, 2020, 28(9): 1-8 (in Chinese). 沈涛, 李舜酩, 辛玉. 基于深度学习的旋转机械故障诊断研究综述[J]. 计算机测量与控制, 2020, 28(9): 1-8. [34] LEI Y G, JIA F, ZHOU X, et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering, 2015, 51(21): 49-56 (in Chinese). 雷亚国, 贾峰, 周昕, 等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21): 49-56. [35] WANG X Y, LI C F, LI Z S. Research of aero- engine gas path fault diagnosis based on SOM network and synergetic theory[J]. Computer Measurement & Control, 2014, 22(2): 319-320, 328 (in Chinese). 王修岩, 李萃芳, 李宗帅. 基于SOM和协同学的航空发动机气路故障诊断研究[J]. 计算机测量与控制, 2014, 22(2): 319-320, 328. [36] PANG S, YANG X Y, ZHANG X F. Aero engine component fault diagnosis using multi-hidden-layer extreme learning machine with optimized structure[J]. International Journal of Aerospace Engineering, 2016, 2016: 1329561. [37] 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. [38] LI Z, ZHONG S S, LIN L. Novel gas turbine fault diagnosis method based on performance deviation model[J]. Journal of Propulsion and Power, 2016, 33(3): 730-739. [39] PENG J, GUO C Y, ZHANG Y, et al. Fault diagnosis of aero engine components based on deep learning method[J]. System Simulation Technology, 2018, 14(1): 20-24 (in Chinese). 彭军, 郭晨阳, 张勇, 等. 基于深度学习的航空发动机部件故障诊断[J]. 系统仿真技术, 2018, 14(1): 20-24. [40] JIANG L Y, LI W L, CUI J G, et al. Fault diagnosis of aero-engine gas path system based on PCA and DBN[J]. Journal of Shenyang Aerospace University, 2019, 36(1): 57-62 (in Chinese). 蒋丽英, 栗文龙, 崔建国, 等. 基于PCA与DBN的航空发动机气路系统故障诊断[J]. 沈阳航空航天大学学报, 2019, 36(1): 57-62. [41] JIANG L Y, PENG C Y, CUI J G, et al. Fault diagnosis of aero-engine air circuit system based on reliefF-PNN[J]. Journal of Shenyang Aerospace University, 2018, 35(4): 77-84 (in Chinese). 蒋丽英, 彭昌毅, 崔建国, 等. 基于ReliefF-PNN的发动机气路系统故障诊断[J]. 沈阳航空航天大学学报, 2018, 35(4): 77-84. [42] FU X Y, LUO H, ZHONG S S, et al. Aircraft engine fault detection based on grouped convolutional denoising autoencoders[J]. Chinese Journal of Aeronautics, 2019, 32(2): 296-307. [43] LIU J, LIU J F, YU D R, et al. Fault detection for gas turbine hot components based on a convolutional neural network[J]. Energies, 2018, 11(8): 2149. [44] ZHONG S S, FU S, LIN L. A novel gas turbine fault diagnosis method based on transfer learning with CNN[J]. Measurement, 2019, 137: 435-453. [45] SHEN Z J, WAN X L, YE F, et al. Deep learning based framework for automatic damage detection in aircraft engine borescope inspection[C]//2019 International Conference on Computing, Networking and Communications (ICNC). Piscataway: IEEE Press, 2019: 1005-1010. [46] CAO M, WANG K W, FUJⅡ Y, et al. Development of a friction component model for automotive powertrain system analysis and shift controller design based on parallel-modulated neural networks[J]. Journal of Dynamic Systems, Measurement, and Control, 2005, 127(3): 382-405. [47] CAO M, WANG K W, DEVRIES L, et al. Experimental characterization and gray-box modeling of spool-type automotive variable-force-solenoid valves with circular flow ports and notches[J]. Journal of Dynamic Systems, Measurement, and Control, 2006, 128(3): 636-654. [48] GRIEVES M. Origins of the digital twin concept[EB/OL]. (2016-08)[2021-03-24]. https://www.researchgate.net/publication/307509727_Origins_of_the_Digital_Twin_Concept. [49] MUSSOMELI A, PARROTT A, COTTELEER M. Industry 4.0 and the digital twin - Manufacturing meets its match[EB/OL]. (2017-05-12)[2021-03-24]. https://www2.deloitte.com/us/en/insights/focus/industry-4-0/digital-twin-technology-smart-factory.html. [50] DOMONE J. Digital twin for life predictions in civil aerospace[EB/OL]. (2018-06)[2021-03-24]. https://-www.snclavalin.com/~/media/Files/S/SNCLavalin/-download-ce-ntre/en/whitepaper/digital%20twin%20-white-paper-v6.pdf. [51] AUWERAER H-V DER. Connecting physics based and data driven models: The best of two worlds[EB/OL]. (2018-03-06)[2021-03-24]. https://www.ima.umn.edu/2017-2018/SW3.6-8.18/26842. [52] LIU D T, GUO K, WANG B K, et al. Summary and perspective survey on digital twin technology[J]. Chinese Journal of Scientific Instrument, 2018, 39(11): 1-10 (in Chinese). 刘大同, 郭凯, 王本宽, 等. 数字孪生技术综述与展望[J]. 仪器仪表学报, 2018, 39(11): 1-10. [53] ZHUANG C B, LIU J H, XIONG H, et al. Connotation, architecture and trends of product digital twin[J]. Computer Integrated Manufacturing Systems, 2017, 23(4): 753-768 (in Chinese). 庄存波, 刘检华, 熊辉, 等. 产品数字孪生体的内涵、体系结构及其发展趋势[J]. 计算机集成制造系统, 2017, 23(4): 753-768. [54] TAO F, LIU W R, LIU J H, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18 (in Chinese). 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18. [55] NORÉN P. The digital airline[R]. 2012. [56] ROSS C. Digital twin[R]. Berlin: IAQS Supplier Forum, 2019. [57] WANG L. Research on the docking technology of final installation for aeroengine low pressure turbine unit based on digital twin[J]. Computer Measurement & Control, 2018, 26(10): 286-290, 303 (in Chinese). 王岭. 基于数字孪生的航空发动机低压涡轮单元体对接技术研究[J]. 计算机测量与控制, 2018, 26(10): 286-290, 303. [58] LIU T, ZHANG J C, LIU K. Aero engine life cycle management based on digital twin[J]. Aerospace Power, 2018(1): 52-56 (in Chinese). 刘婷, 张建超, 刘魁. 基于数字孪生的航空发动机全生命周期管理[J]. 航空动力, 2018(1): 52-56. [59] LV Y Q. Exploring the digital twin mode for commercial aero-engine production management and control[J]. Computer Measurement & Control, 2019, 27(3): 254-258 (in Chinese). 吕延全. 商用航空发动机生产管控的数字孪生模式探索[J]. 计算机测量与控制, 2019, 27(3): 254-258. [60] CUI Y H, YANG B T, FANG Y, et al. Application of digital twin technology in aeroengine smart production line[J]. Aeroengine, 2019, 45(5): 93-96 (in Chinese). 崔一辉, 杨滨涛, 方义, 等. 数字孪生技术在航空发动机智能生产线中的应用[J]. 航空发动机, 2019, 45(5): 93-96. [61] LIU K, WANG P, LIU T. The application of digital twin in aero engine operation and maintenance[J]. Aerospace Power, 2019(4): 70-74 (in Chinese). 刘魁, 王潘, 刘婷. 数字孪生在航空发动机运行维护中的应用[J]. 航空动力, 2019(4): 70-74. [62] JI S X, PAN S R, CAMBRIA E, et al. A survey on knowledge graphs: representation, acquisition, and applications[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 494-514. [63] YAN J H, WANG C Y, CHENG W L, et al. A retrospective of knowledge graphs[J]. Frontiers of Computer Science, 2018, 12(1): 55-74. [64] SUN T, WANG Q. Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)[C]//International Conferences on Software Engineering and Knowledge Engineering, 2019: 177-232. [65] LIU R H, XIE G Q, YUAN Z G, et al. Research on intelligent fault diagnosis based on knowledge graph[J]. Designing Techniques of Posts and Telecommunications, 2020(10): 30-35 (in Chinese). 刘瑞宏, 谢国强, 苑宗港, 等. 基于知识图谱的智能故障诊断研究[J]. 邮电设计技术, 2020(10): 30-35. [66] ZHAO Q. Building and application of CNC equipment fault knowledge graph[J]. Aeronautical Manufacturing Technology, 2020, 63(3): 96-102 (in Chinese). 赵倩. 数控设备故障知识图谱的构建与应用[J]. 航空制造技术, 2020, 63(3): 96-102. [67] ZHANG M J, XU N, HU J H, et al. Knowledge graph construction and intelligent question answering for transformer operation and maintenance[J]. Journal of Global Energy Interconnection, 2020, 3(6): 607-617 (in Chinese). 张敏杰, 徐宁, 胡俊华, 等. 面向变压器智能运检的知识图谱构建和智能问答技术研究[J]. 全球能源互联网, 2020, 3(6): 607-617. [68] SUAREZ E L, DUFFY M J, GAMACHE R N, et al. Jet engine life prediction systems integrated with prognostics health management[C]//2004 IEEE Aerospace Conference Proceedings. Piscataway: IEEE Press, 2004. [69] SAE Committee E-32. Aircraft gas turbine engine health management system guide: AIR1587B-2007[S]. Warrendale: SAE International, 2007. [70] NASA. NASA Aviation Safety Program (AVSP)[EB/OL]. (2010-1-10)[2021-03-24]. http://avsp.larc.nasa.gov. [71] SAE Committee E-32. guide to life usage monitoring and parts management for aircraft gas turbine engines: AIR1872A-1998[S]. Warrendale: SAE International, 2005: 1-30. [72] VITTAL S, HAJELA P, JOSHI A. Review of approaches to gas turbine life management[C]//10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston: AIAA, 2004. [73] PATRICK R, SMITH M J, BYINGTON C S, et al. Integrated software platform for fleet data analysis, enhanced diagnostics, and safe transition to prognostics for helicopter component CBM[C]//2010 Annual Conference Proceedings of the Prognostics and Health Management Society. Rochester: PHM Society, 2010: 1-16. [74] BANKS J, REICHARD K, CROW E, et al. How engineers can conduct cost-benefit analysis for PHM systems[C]//IEEE Aerospace and Electronic Systems Magazine. Piscataway: IEEE Press, 2009: 22-30. [75] LEAO B P, FITZGIBBON K T, PUTTINI L C, et al. Cost-benefit analysis methodology for PHM applied to legacy commercial aircraft[C]//2008 IEEE Aerospace Conference. Piscataway: IEEE Press, 2008: 1-13. [76] BEALE J K, HESS A. Experiences with A-7E and the AV-8B engine monitoring systems: the good and the ugly[C]//2000 IEEE Aerospace Conference. Proceedings. Piscataway: IEEE Press, 2000: 221-227. [77] JAW L C, WU D N, BRYG D. Tracking and control of gas turbine component damage/life[C]//Proceedings of NATO AVT Panel symposium on Aging Mechanisms and Control, 2001. [78] JAW L C, BRYG D, KHALAK A. Concept and prototype of GCSS-AF research environment for condition based and reliability centered maintenance[C]//2007 Integrated System Health Management (ISHM) Conference, 2007. [79] ASTM Committee. Standard practices for cycle counting in fatigue analysis: E1049-85 (2005)[S]. West Conshohocken: ASTM International, 2005. [80] Royal Aeronautical Society. ESDU 06010 cycle counting methods for the estimation of fatigue life[S]. Houndsditch: HIS ESDU, 2006. [81] WISEMAN M W, GUO T H. An investigation of life extending control techniques for gas turbine engines[C]//Proceedings of the 2001 American Control Conference. Piscataway: IEEE Press, 2001. [82] GUO T H. A roadmap for aircraft engine life extending control[C]//Proceedings of the 2001 American Control Conference. Piscataway: IEEE Press, 2001. [83] GUO T H, CHEN P, JAW L. Intelligent life-extending controls for aircraft engines[C]//AIAA 1st Intelligent Systems Technical Conference. Reston: AIAA, 2004. [84] ABU A O, ESHATI S, LASKARIDIS P, et al. Aero-engine turbine blade life assessment using the Neu/Sehitoglu damage model[J]. International Journal of Fatigue, 2014, 61: 160-169. [85] XI Z M, JING R, WANG P F, et al. A copula-based sampling method for data-driven prognostics[J]. Reliability Engineering & System Safety, 2014, 132: 72-82. [86] LIU J Q, ZHANG M L, ZUO H F, et al. Remaining useful life prognostics for aeroengine based on superstatistics and information fusion[J]. Chinese Journal of Aeronautics, 2014, 27(5): 1086-1096. [87] MOSALLAM A, MEDJAHER K, ZERHOUNI N. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction[J]. Journal of Intelligent Manufacturing, 2016, 27(5): 1037-1048. [88] RAGAB A, OUALI M S, YACOUT S, et al. Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan-Meier estimation[J]. Journal of Intelligent Manufacturing, 2016, 27(5): 943-958. [89] LI Y G. Performance-analysis-based gas turbine diagnostics: a review[J]. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 2002, 216(5): 363-377. [90] LU C J, MEEKER W O. Using degradation measures to estimate a time-to-failure distribution[J]. Technometrics, 1993, 35(2): 161-174. [91] CRK V. Reliability assessment from degradation data[C]//Annual Reliability and Maintainability Symposium. 2000 Proceedings. Piscataway: IEEE Press, 2000: 155-161. [92] AL-DAHIDI S, DI MAIO F, BARALDI P, et al. Remaining useful life estimation in heterogeneous fleets working under variable operating conditions[J]. Reliability Engineering & System Safety, 2016, 156: 109-124. [93] HU C, YOUN B D, WANG P F, et al. An ensemble approach for robust data-driven prognostics[C]//Proceedings of ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2013: 333-347. [94] DAVID H A. The theory of competing risks[J]. Australian Journal of Statistics, 1976, 18(3): 101-110. [95] MISRA K B. Reliability analysis and prediction: a methodology oriented treatment[M]. Amsterdam: Elsevier, 1992. [96] TERESA LAM C, YEH R H. Optimal replacement policies for multistate deteriorating systems[J]. Naval Research Logistics, 1994, 41(3): 303-315. [97] KLUTKE G A, YANG Y. The availability of inspected systems subject to shocks and graceful degradation[J]. IEEE Transactions on Reliability, 2002, 51(3): 371-374. [98] PHAM H, XIE M G. A generalized surveillance model with applications to systems safety[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2002, 32(4): 485-492. [99] HUANG W, ASKIN R G. Reliability analysis of electronic devices with multiple competing failure modes involving performance aging degradation[J]. Quality and Reliability Engineering International, 2003, 19(3): 241-254. [100] ZUO M J, JIANG R Y, YAM R C M. Approaches for reliability modeling of continuous-state devices[J]. IEEE Transactions on Reliability, 1999, 48(1): 9-18. [101] HOSSEINI M M, KERR R M, RANDALL R B. An inspection model with minimal and major maintenance for a system with deterioration and Poisson failures[J]. IEEE Transactions on Reliability, 2000, 49(1): 88-98. [102] BUNEA C, MAZZUCHI T A. Bayesian accelerated life testing under competing failure modes[C]//Annual Reliability and Maintainability Symposium, 2005. Piscataway: IEEE Press, 2005: 152-157. [103] AMARI S V, MISRA R B. Comment on: Dynamic reliability analysis of coherent multistate systems[J]. IEEE Transactions on Reliability, 1997, 46(4): 460-461. [104] LI W J, PHAM H. Reliability modeling of multi-state degraded systems with multi-competing failures and random shocks[J]. IEEE Transactions on Reliability, 2005, 54(2): 297-303. [105] JAW L C, WANG W. Mathematical formulation of model-based methods for diagnostics and prognostics[C]//Proceedings of ASME Turbo Expo 2006: Power for Land, Sea, and Air, 2008: 691-697. [106] SMITH G, SCHROEDER J B, NAVARRO S, et al. Development of a prognostics and health management capability for the Joint Strike Fighter[C]//1997 IEEE Autotestcon Proceedings AUTOTESTCON'97. Piscataway: IEEE Press, 1997: 676-682. [107] SCHEUREN W, CALDWELL K, GOODMAN G, et al. Joint strike fighter prognostics and health management[C]//34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. Reston: AIAA, 1998: 3710. [108] SIMON D. An overview of the NASA aviation safety program propulsion health monitoring element[C]//36th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit. Reston: AIAA, 2000. [109] LI Y F, LV Z Q, CAI W, et al. Fatigue life analysis of turbine disks based on load spectra of aero-engines[J]. International Journal of Turbo & Jet-Engines, 2016, 33(1): 22-33. [110] FENG D L, XIAO M Q, LIU Y X, et al. A kernel principal component analysis-based degradation model and remaining useful life estimation for the turbofan engine[J]. Advances in Mechanical Engineering, 2016, 8(5): 168781401665016. [111] LIU L S, WANG S J, LIU D T, et al. Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine[J]. Microelectronics Reliability, 2015, 55(9-10): 2092-2096. [112] LU F, HUANG J Q, LV Y Q. Gas path health monitoring for a turbofan engine based on a nonlinear filtering approach[J]. Energies, 2013, 6(1): 492-513. [113] YUAN M, WU Y T, LIN L. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network[C]//2016 IEEE International Conference on Aircraft Utility Systems. Piscataway: IEEE Press, 2016: 135-140. [114] HU H L, ZUO H F, REN S H. Performance reliability evaluation for aviation engines based on stochastic process[J]. Science & Technology Information, 2009(29): 17-18 (in Chinese). 胡昊磊, 左洪福, 任淑红. 基于随机过程的航空发动机性能可靠性预测[J]. 科技信息, 2009(29): 17-18. [115] REN S H, ZUO H F, BAI F. Real-time performance reliability prediction for civil aviation engines based on Brownian motion with drift[J]. Journal of Aerospace Power, 2009, 24(12): 2796-2801 (in Chinese). 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. [116] ZHOU Y H, KANG R. Degradation model and application in life prediction of rotating-mechanism[J]. Chinese Journal of Nuclear Science and Engineering, 2009, 29(2): 146-151 (in Chinese). 周玉辉, 康锐. 基于退化失效模型的旋转机械寿命预测方法[J]. 核科学与工程, 2009, 29(2): 146-151. [117] FU H M, YUE X R, ZHANG Y B. Reliability assessment and life prediction for very few failure data[J]. Journal of Aerospace Power, 2010, 25(8): 1855-1859 (in Chinese). 傅惠民, 岳晓蕊, 张勇波. 极少失效数据的可靠性评估和寿命预测[J]. 航空动力学报, 2010, 25(8): 1855-1859. [118] ZHANG X F, ZHAO Y, WANG S C, et al. Weibull reliability analysis in small samples based on SVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2012, 31(8): 1359-1362, 1368 (in Chinese). 张新锋, 赵彦, 王生昌, 等. 基于支持向量机的小样本威布尔可靠性分析[J]. 机械科学与技术, 2012, 31(8): 1359-1362, 1368. [119] CHENG W M, FENG J, ZHOU J L, et al. MMLE-Bayes reliability assessment method for aerospace products with weibull distribution[J]. Telecommunication Engineering, 2006, 46(6): 22-27 (in Chinese). 程皖民, 冯静, 周经伦, 等. Weibull分布航天产品可靠性评估的MMLE-Bayes方法[J]. 电讯技术, 2006, 46(6): 22-27. [120] TANG J, ZHANG M Q. Research on small-sample fuzzy reliability simulation based on Bayes Monte Carlo method[J]. Journal of System Simulation, 2009, 21(23): 7557-7559, 7563 (in Chinese). 唐俊, 张明清. 基于Bayes Monte Carlo方法的小样本模糊可靠性仿真研究[J]. 系统仿真学报, 2009, 21(23): 7557-7559, 7563. [121] HAN M. Bayesian reliability analysis of zero failure data of a certain model engine[J]. Acta Aeronautica et Astronautica Sinica, 1999, 20(3): 216-219 (in Chinese). 韩明. 某型发动机无失效数据的Bayes可靠性分析[J]. 航空学报, 1999, 20(3): 216-219. [122] HAN M. Reliability parameter estimation based on non-failure data[M]. Beijing: China Statistics Press, 2005 (in Chinese). 韩明. 基于无失效数据的可靠性参数估计[M]. 北京: 中国统计出版社, 2005. [123] HAN M. E-Bayes reliability analysis for liquid rocket engine with zero-failure data[J]. Acta Aeronautica et Astronautica Sinica, 2011, 32(12): 2213-2219 (in Chinese). 韩明. 无失效数据下液体火箭发动机的E-Bayes可靠性分析[J]. 航空学报, 2011, 32(12): 2213-2219. [124] LI W. Research on aero-engine reliability assessment based on competing failure[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2013 (in Chinese). 李伟. 基于竞争失效的航空发动机可靠性评估研究[D]. 南京: 南京航空航天大学, 2013. [125] WANG H W, GAO J, WU H Q. Residual remaining life prediction based on competing failures for aircraft engines[J]. Journal of Mechanical Engineering, 2014, 50(6): 197-205 (in Chinese). 王华伟, 高军, 吴海桥. 基于竞争失效的航空发动机剩余寿命预测[J]. 机械工程学报, 2014, 50(6): 197-205. [126] 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. [127] LITT J S, SIMON D L, GARG S, et al. A survey of intelligent control and health management technologies for aircraft propulsion systems[J]. Journal of Aerospace Computing, Information, and Communication, 2004, 1(12): 543-563. [128] WU D N, JAW L C. Intelligent engine life usage control (ILEC Program) demonstration-final: NAS3-98112[R]. 2002. [129] JAW L C. Putting CBM and EHM in perspective[J]. Maintenance Technology, 2001, 11: 14-18. [130] WANG Y, JAW L, RENDEK P, et al. Demonstration of A reliability centered maintenance (RCM) tool to extend engine's time-on-wing (TOW)[C]//2007 IEEE Aerospace Conference. Piscataway: IEEE Press, 2007: 1-5. [131] KHALAK A, TIERNO J. Influence of prognostic health management on logistic supply chain[C]//2006 American Control Conference. Piscataway: IEEE Press, 2006. [132] LI J Z, WANG G H, WU L F, et al. On-line fault-diagnosis study: Model-based fault diagnosis for ultracapcitors[C]//2014 Prognostics and System Health Management Conference (PHM-2014 Hunan). Piscataway: IEEE Press, 2014: 158-162. [133] WANG C, LI Y H, ZHANG H X. The remote diagnosis and maintenance of the engine[J]. Journal of Institute of Command and Technology, 2003, 14(3): 42-45 (in Chinese). 汪诚, 李应红, 张恒喜. 航空发动机远程智能故障诊断与维修[J]. 装备指挥技术学院学报, 2003, 14(3): 42-45. [134] P&W. PW4056-94 maintenance planning guide[R]. 2005. [135] GE Transportation. CF34 workscope planning guide[R]. General Electric Company, 2006. [136] LIANG J. On-condition maintenance decision research of aero-engine based on cost optimization[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2005 (in Chinese). 梁剑. 基于成本优化的民用航空发动机视情维修决策研究[D]. 南京: 南京航空航天大学, 2005. [137] ZHANG H J. Research on methods of performance synthetic estimation and CBM decision-making models for aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2007 (in Chinese). 张海军. 民航发动机性能评估方法与视情维修决策模型研究[D]. 南京: 南京航空航天大学, 2007. [138] ZHANG H J, ZUO H F, LIANG J. Maintenance level decision of aero-engine based on vprs theory[J]. Transactions of Nanjing University of Aeronautics & Astronautics, 2005, 22(4): 281-284. [139] ZHENG B. Investigation on aeroengine maintenance level decision based on PSO-SVM[J]. Journal of Propulsion Technology, 2013, 34(5): 687-692 (in Chinese). 郑波. 基于PSO-SVM的民航发动机送修等级决策研究[J]. 推进技术, 2013, 34(5): 687-692. [140] FU X Y, ZHONG S S. Heuristic algorithm for solving the aeroengine maintenance scheduling problem[J]. Computer Integrated Manufacturing Systems, 2010, 16(7): 1552-1557 (in Chinese). 付旭云, 钟诗胜. 民用航空发动机维修计划启发式算法[J]. 计算机集成制造系统, 2010, 16(7): 1552-1557. [141] FU X Y, CUI Z Q, ZHONG S S. Civil aeroengine workscope decision-making under uncertain conditions[J]. Journal of Harbin Institute of Technology, 2012, 44(7): 78-82 (in Chinese). 付旭云, 崔智全, 钟诗胜. 不确定条件下民航发动机维修工作范围决策[J]. 哈尔滨工业大学学报, 2012, 44(7): 78-82. [142] HAO Y, SUN J G, BAI J. State-of-the-art and prospect of aircraft engine fault diagnosis using gas path parameters[J]. Journal of Aerospace Power, 2003, 18(6): 753-760 (in Chinese). 郝英, 孙健国, 白杰. 航空燃气涡轮发动机气路故障诊断现状与展望[J]. 航空动力学报, 2003, 18(6): 753-760. [143] Beijing Rand star arrow Technology Co., Ltd. Equipment life determination, life extension and maintenance optimization system[R]. 2013 (in Chinese). 北京兰德星箭科技有限公司. 装备定寿、延寿及维修优化系统[R]. 2013. [144] DING G. Research on health assessment and maintenance decision support technology of aeroengine unit[D]. Harbin: Harbin Institute of Technology, 2011 (in Chinese). 丁刚. 航空发动机单元体健康状态评估与维修决策支持技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2011. [145] ZHONG S S, LUAN S G. Aeroengine condition monitoring and maintenance data processing system oriented to lifecycle management[J]. Computer Integrated Manufacturing Systems, 2006, 12(8): 1273-1278 (in Chinese). 钟诗胜, 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. [146] 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. [147] RONG X. Methods of life prediction and mainteance decision making in civil aeroengine health management[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008 (in Chinese). 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]. 南京: 南京航空航天大学, 2008. [148] REN S H. Research on methods of performance reliability assessments and life on wing prediction for civil aeroengine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2010 (in Chinese). 任淑红. 民航发动机性能可靠性评估与在翼寿命预测方法研究[D]. 南京: 南京航空航天大学, 2010. [149] ZHU L. On-wing performance evaluation and route maintenance management of civil aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014 (in Chinese). 朱磊. 民用航空发动机翼上性能评估及航线维修管理方法研究[D]. 南京: 南京航空航天大学, 2014. [150] HU H L. Research on the prediction technology of engine removal in the condition based maintenance management of aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2009 (in Chinese). 胡昊磊. 航空发动机视情维修管理中的下发预测技术研究[D]. 南京: 南京航空航天大学, 2009. |
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