226 A Bispectrum Feature Extracting and LVQ Neural Network Based Structure Damage Detection Method

DOI Web Site
  • CHEN Jin
    The State Key Laboratory of Vibration, Shock & Noise, Shanghai Jiao Tong University
  • SU Xiang
    Department of Mechanical Engineering, Tokyo Institute of Technology
  • HAGIWARA Ichiro
    Department of Mechanical Engineering, Tokyo Institute of Technology
  • SHI Qinzhong
    NASDA

Bibliographic Information

Other Title
  • 226 動的逆問題解析を用いた欠陥同定

Description

The subject of structure defective diagnosis is an active research area in nondestructive testing (NDT). In this paper, a new structure damage detecting approach is proposed based on the combination of the bispectrum feature extracting method and the Learning Vector Quantization (LVQ) identification method. Since that bispectrum analysis possesses the capability of Gaussian noise restraining, so it is available to be employed to enhance the performance of feature extracting. In simulation, by using the method proposed, it has shown that relatively very higher accuracy of structure damage identification can be obtained comparing with the MAC (formed from the Model parameters) method, especially in case of low signal to noise ratio environment.

Journal

Keywords

Details 詳細情報について

Report a problem

Back to top