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Volume 40 Issue 10
Oct.  2014
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Journal of Beijing University of Aeronautics and Astronautics  > 2014  >  40(10): 1366-1371.
Gao Xiaoxia, Huo Weigang, Feng Xingjieet al. Civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(10): 1366-1371. doi: 10.13700/j.bh.1001-5965.2013.0656(in Chinese)
Citation: Gao Xiaoxia, Huo Weigang, Feng Xingjieet al. Civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(10): 1366-1371. doi: 10.13700/j.bh.1001-5965.2013.0656(in Chinese)
shu

Civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier

doi: 10.13700/j.bh.1001-5965.2013.0656

  • School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

  • Received Date: 18 Nov 2013
  • Publish Date: 20 Oct 2014
    Abstract
  • Most of current civil aircraft's exceedance event intelligent diagnosis models are "black box" model, which donot contribute to analyze the occurrence of civil aircraft's exceedance event. In order to overcome these shortcomings, a civil aircraft's exceedance event diagnosis method based on fuzzy associative classifier (FAC) was proposed. First, quick access recorder's (QAR's) parameters value when exceedance event occured was extracted.Fuzzy C-means (FCM) cluster algorithm was adopted to preprocess extracted QAR's parameters value. Then, the library of fuzzy associative classification rule (FACR) was generated by Apriori algorithm.Genetic algorithm was used to prune the library of FACR.Finally,fuzzy classification reasoning method was integrated to build FAC. The FAC was verified with sample data generated by B737-800. Experiment results show that the FAC can diagnose exceedance event effectively, and its classification error rate is equivalent to least squares support vector machine (LS-SVM), but its interpretability is superior to LS-SVM.

     

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