Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
381474 | Engineering Applications of Artificial Intelligence | 2011 | 6 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yi-Yan Liu, Yong-Feng Ju, Chen-Dong Duan, Xue-Feng Zhao,