Volume 39 Issue 9
Sep.  2013
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Li Yebo, Li Qiuhong, Huang Xianghua, et al. Fault diagnosis for sensors and components of aero-engine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(9): 1174-1180. (in Chinese)
Citation: Li Yebo, Li Qiuhong, Huang Xianghua, et al. Fault diagnosis for sensors and components of aero-engine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(9): 1174-1180. (in Chinese)

Fault diagnosis for sensors and components of aero-engine

  • Received Date: 17 Oct 2012
  • Publish Date: 30 Sep 2013
  • According to local learning and ensemble learning technologies, a method for sensors fault and abrupt components fault diagnosis of aero-engine was proposed based on support vector machine-extreme learning machine-Kalman filter (SVM-ELM-KF). The training approach of improved recursive reduced-least squares support vector regression (IRR-LSSVR) was extended to classification machine to distinguish sensor faults and component faults. The training method makes the classification machine have better sparsity. Considering sensors fault, the ELM was used for fault location. For components fault, the improved KF was adopted for health parameters estimation and fault location. Simulation results show that the proposed method for fault diagnosis can distinguish sensor faults and abrupt component faults accurately, and locate the faults effectively. That is, the proposed method is valid.

     

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  • [1] Garg S.Controls and health management technologies for intelligent aerospace propulsion systems[R].AIAA-2004-0949,2004 [2] Sanjay G.Controls and health management technologies for intelligent aerospace propulsion systems[R].AIAA-2004-0949,2004 [3] Link C.Recent advancements in aircraft engine health management (EHM) technologies and recommendations for next step[R].ASME GT2005-68625,2005 [4] Kobayashi T,Simon D L.Evaluation of an enhanced band of Kalman filters for in-flight aircraft engine fault sensor fault diagnostics[J].Engineering for Gas Turbines and Power,2005,127: 497-504 [5] 覃道亮,何皑,孔祥兴,等.基于UIO的航空发动机控制系统传感器故障诊断[J].航空动力学报,2011,26(6):1396-1404 Tan Daoliang,He Ai,Kong Xiangxing,et al.UIO-based sensor fault diagnosis for aero-engine control systems[J].Journal of Aerospace Power,2011,26(6):1396-1404 (in Chinese) [6] 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 Chen Tian,Sun Jianguo,Hao Ying.Neural network and dempster-shafter theory based fault diagnosis for aero-engine gas path[J].Journal of Aerospace Power,2006,27(6):1014-1017(in Chinese) [7] 鲁峰,黄金泉,陈煜,等.基于SPSO-SVR的融合航空发动机传感器故障诊断[J].航空动力学报,2009,24(8):1856-1865 Lu Feng,Huang Jinquan,Chen Yu,et al.Research on performance fault fusion diagnosis of aero-engine component[J].Journal of Aerospace Power,2009,24(8):1856-1865(in Chinese) [8] 仇小杰,黄金泉,鲁峰,等.基于云关联度的航空发动机传感器、部件故障识别系统设计[J].航空动力学报,2011, 26(11):2584-2592 Qiu Xiaojie,Huang Jinquan,Lu Feng,et al.Fault diagnosis system design for the sensors and components of aircraft engine based on cloud relational analysis[J].Journal of Aerospace Power,2011,26(11):2584-2592(in Chinese) [9] Bottou L,Vapnik V.Local learning algorithms[J].Neural Computation,1992,6(4):888-900 [10] Freund Y,Schapire R E.A decision-theoretic generalization of on-line learning and an application to boosting[J].Journal of Computer and System Sciences,1997,55(1):119-139 [11] Vapnik V.The nature of statical learning theory[M].NewYork,USA:Springer-Verlag,1995 [12] Suykens J A K,Vandewalle J.Least square support vector machine[J].Neural Processing Letters,1999,9(3):293-300 [13] Zhao Yong ping,Sun Jianguo,Du Zhonghua,et al.An improved recursive least square support vector regression[J].Neurocomputing,2012,87:1-9 [14] Huang Guangbin,Zhu Qinyu,Siew C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70:489-501
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