Volume 29 Issue 2
Feb.  2014
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Article Navigation >  Journal of Aerospace Power  > 2014  >  29(2): 451-457
ZHAO Cheng-wei, YE Zhi-feng, WANG Ji-qiang, YIN Bin-bin. Rapid prototype real-time simulation of turbo-fan engine sensor fault diagnosis[J]. Journal of Aerospace Power, 2014, 29(2): 451-457. doi: 10.13224/j.cnki.jasp.2014.02.026
Citation: ZHAO Cheng-wei, YE Zhi-feng, WANG Ji-qiang, YIN Bin-bin. Rapid prototype real-time simulation of turbo-fan engine sensor fault diagnosis[J]. Journal of Aerospace Power, 2014, 29(2): 451-457. doi: 10.13224/j.cnki.jasp.2014.02.026
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Rapid prototype real-time simulation of turbo-fan engine sensor fault diagnosis

doi: 10.13224/j.cnki.jasp.2014.02.026

  • College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

  • Received Date: 2012-12-12
  • Publish Date: 2014-02-28
    Abstract
  • Centered on NI (National Instruments) CompactRIO, a real-time simulation platform with rapid prototyping was built to validate the fault diagnosis approaches for turbo-fan engine sensors rapidly and efficiently. Based on a bank of Kalman filters, an turbo-fan engine sensor fault diagnostics system was developed in LabVIEW environments. Extensive experiments were conducted for the fault detection, isolation and restructuring of individual sensor faults, at both steady state and dynamic conditions. The accuracy of the proposed algorithm was thus verified. Experimental results showed that Kalman filter algorithm could ensure safe operation of engine control system even in the case of sensor failures, the best accuracy of diagnosis reached 1.4%, and the effectiveness of this rapid prototype real-time platform for sensor fault diagnosis was also shown. Consequently, the developed platform could be taken as the foundation for semi-physical tests.

     

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