[1] Garg S. Controls and health management technologies for intelligent aerospace propulsion systems, AIAA-2004-0949. Reston: AIAA, 2004.[2] Delaat J C, Merrill W C. Advanced detection, isolation, and accommodation of sensor failures in turbofan engines, NASA-TP-2925. Washington, D.C.: NASA, 1990.[3] Garg S, Schadow K, Horn W, et al. Sensor and actuator needs for more intelligent gas turbine engines, NASA/TM-2010-216746. Washington, D.C.: NASA, 2010.[4] Xu Q H, Shi J. Fault diagnosis for aero-engine applying a new multi-class support vector algorithm[J]. Chinese Journal of Aeronautics, 2006, 19(1): 175-182.[5] Alag G, Gilyard G. A proposed Kalman filter algorithm for estimation of unmeasured output variables for an F100 turbofan engine, AIAA-1990-1920. Reston: AIAA, 1990.[6] Kobayashi T, Simon D L. Application of a bank of Kalman filters for aircraft engine fault diagnositcs, NASA/TM-2003-212526. Washington, D.C.: NASA, 2003.[7] Mattern D L, Jaw L C, Guo T H, et al. Using neural networks for sensor validation, AIAA-1998-3547. Reston: AIAA, 1998.[8] Lu F, Huang J Q. Engine component performance prognostics based on decision fusion[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(10): 1795-1800. (in Chinese) 鲁峰, 黄金泉. 航空发动机部件性能参数融合预测[J]. 航空学报, 2009, 30(10): 1795-1800.[9] Fu Q, Su B K. Study of fault-diagnosing system of turntable based on hierarchical information fusion[J]. Journal of Harbin Institute of Technology, 2006, 38(11): 1906-1909.(in Chinese) 付强, 苏宝库. 基于多级信息融合的转台故障诊断系统研究[J]. 哈尔滨工业大学学报, 2006, 38(11): 1906-1909.[10] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1-3): 489-501.[11] Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4): 341-359.[12] Litt J S. An optimal orthogonal decomposition method for Kalman filter-based turbofan engine thrust estimation, NASA/TM-2005-213864. Washington, D.C.: NASA, 2005.[13] Zhao Y P, Sun J G, Du Z H, et al. An improved recursive reduced least square support vector regression[J]. Neurocomputing, 2012, 87: 1-9.[14] Zhu Q Y, Qin A K, Suganthan P N, et al. Evolutionary extreme learning machine[J]. Pattern Recognition, 2005, 38(10): 1759-1763.[15] Bartlett P L. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the network[J]. IEEE Transactions on Information Theory, 2005, 44(2): 525-536.[16] Li Y B. Componentization modeling and performance parameters estimation of aero-engine. Nanjing: College of Energy & Power Engineering, Nanjing University of Aeronautics and Astronautics, 2011. (in Chinese) 李业波. 航空发动机组件化建模及性能参数估计.南京: 南京航空航天大学能源与动力学院, 2011.[17] Zheng D Z. Linear system theory[M]. Beijing: Tsinghua University Press, 2004: 70-117. (in Chinese) 郑大钟. 线性系统理论[M]. 北京: 清华大学出版社,2002: 70-117.[18] Zhao Y P, Sun J G. Recursive reduced least square support vector regression[J]. Pattern Recognition, 2009, 42(5): 837-842.[19] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letter, 1999, 9(3): 293-300.[20] Zhang X D. Matrix analysis and applications[M]. Beijing: Tsinghua University Press, 2004: 68-69. (in Chinese) 张贤达. 矩阵分析与应用[M]. 北京: 清华大学出版社, 2004: 68-69. |