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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