Enhanced Modal Wavelet Analysis for Damage Detection in Beams

Abstract

The wavelet transform has proven to be a useful mathematical tool to detect changes in the mode shapes of a structure and therefore to detect damage. The authors have proposed a damage detection methodology based on the wavelet analysis of the difference of mode shapes corresponding to a reference state and a potentially damaged state. The wavelet coefficients of each mode shape difference are added up to obtain an overall graphical result along the structure. The coefficients are weighted according to changes in natural frequencies to emphasize the mode shapes most affected by damage. This paper is focused on the enhancement of the damage sensitivity of the methodology. It presents new results when applying a curve fitting approach to reduce experimental noise effect in mode shapes as well as a interpolation technique to virtually increase the geometric sample frequency of the wavelet transform input signal. The enhanced methodology is applied to experimentally tested steel beams with different crack location and depth. The paper analyses the results when considering different number of measuring points. Successful results are obtained using a small number of sensors and mode shapes.