Research on damage detection technique of composite material based on PCAGARSPSVM
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Air Force Aviation University, Changchun 130022, China

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TB33;TP391.9

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    Abstract:

    Aiming at solving lacking of failure data and low efficiency of composite material damage detection, a fault diagnosis method based on principal component analysis(PCA)and support vector machine combined with the rotation symmetric partition(RSPSVM)was proposed. Firstly, the model of uniplanar multielectrode is partitioned into equal area units with rotation symmetry partition, and fault data is acquired adequately. Secondly, genetic algorithm(GA)was introduced into RSPSVM in order to promote the classification performance, and PCA was used to reduce the dimension of feature vector and shorten training time, the final features were put into improved RSPSVM so that PCAGARSPSVM was achieved. Finally, the measured data of three composite material samples were sent to PCAGARSPSVM for verification. After verification of the simulation data and the measured data, the effective certificate of general PCAGARSPSVM algorithm is applied to the diagnosis of damage of aircraft composite material.

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  • Received:
  • Revised:
  • Adopted:
  • Online: November 06,2017
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