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Mathematical Problems in Engineering
Volume 2013, Article ID 352149, 8 pages
http://dx.doi.org/10.1155/2013/352149
Research Article

Locating Impact on Structural Plate Using Principal Component Analysis and Support Vector Machines

1Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau
2Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau

Received 27 January 2013; Accepted 27 March 2013

Academic Editor: Chengjin Zhang

Copyright © 2013 Heming Fu and Qingsong Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A new method which integrates principal component analysis (PCA) and support vector machines (SVM) is presented to predict the location of impact on a clamped aluminum plate structure. When the plate is knocked using an instrumented hammer, the induced time-varying strain signals are collected by four piezoelectric sensors which are mounted on the plate surface. The PCA algorithm is adopted for the dimension reduction of the large original data sets. Afterwards, a new two-layer SVM regression framework is proposed to improve the impact location accuracy. For a comparison study, the conventional backpropagation neural networks (BPNN) approach is implemented as well. Experimental results show that the proposed strategy achieves much better locating accuracy in comparison with the conventional approach.