Locating impact on structural plate using principal component analysis and support vector machines

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. © 2013 Heming Fu and Qingsong Xu.