Structure damage diagnosis using neural network and feature fusion

Authors:
Yi-Yan Liu;Yong-Feng Ju;Chen-Dong Duan;Xue-Feng Zhao
Affiliations:
School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, People's Republic of China;School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, People's Republic of China;School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, People's Republic of China;School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China
Venue:
Engineering Applications of Artificial Intelligence
Year:
2011

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Abstract

A structure damage diagnosis method combining the wavelet packet decomposition, multi-sensor feature fusion theory and neural network pattern classification was presented. Firstly, vibration signals gathered from sensors were decomposed using orthogonal wavelet. Secondly, the relative energy of decomposed frequency band was calculated. Thirdly, the input feature vectors of neural network classifier were built by fusing wavelet packet relative energy distribution of these sensors. Finally, with the trained classifier, damage diagnosis and assessment was realized. The result indicates that, a much more precise and reliable diagnosis information is obtained and the diagnosis accuracy is improved as well.