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Volume 39 Issue 1
Jan.  2013
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Journal of Beijing University of Aeronautics and Astronautics  > 2013  >  (1): 42-46,51.
Lü Yongle, Lang Rongling, Lu Hui, et al. Prediction of aeroengine-s performance parameter combining RBFPN and FAR[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(2): 131-134. (in Chinese)
Citation: Dang Xiangjun, Jiang Tongmin. Degradation prediction based on correlation analysis and assembled neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, (1): 42-46,51. (in Chinese)
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Degradation prediction based on correlation analysis and assembled neural network

  • School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 24 Oct 2011
  • Publish Date: 31 Jan 2013
    Abstract
  • Efficient prognosis for remaining useful life of product is critical for both accelerated degradation testing and prognostics and health management, which are two hot points in recent years. A novel degradation prediction model was proposed to improve the long prediction capability for complex degradation path. Durbin-Watson method and partial correlation graph were utilized to analyze the decomposition results of wavelet transformation. Then, according to the characters of series, back propagation(BP) and wavelet neural network were assembled to predict degradation path. To verify the proposed method, wavelet neural network was selected as comparison. A practical degradation result demonstrates that this model can offer smaller mean square error (MSE) and higher prediction accuracy for remaining useful life (RUL).

     

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    • References(11)
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