Abstract
One of the main challenges in damage detection techniques is sensitivity to damage. During the last years, a large number of papers have used wavelet analysis as a sensitive mathematical tool for identifying changes in mode shapes induced by damage. This paper analyzes the effect of adding a mass to the structure at different positions. Depending on the location and severity of damage, the presence of the mass affects the natural frequencies and mode shapes in a different way. The paper applies a damage detection methodology proposed by the authors, although it has been modified in order to consider the addition of the mas. This methodology is based on a wavelet analysis of the difference of mode shapes of a damaged and a reference state. The singular behavior of a normalized weighted addition of wavelet coefficients is used as an indicator of damage. The presence of damage is detected by combining all the information provided by mode shapes and natural frequencies for different positions of the roving mass. A continuous wavelet transform is used to detect the difference between the response of a healthy state and a damaged one. The paper shows the results obtained for a beam with different cracks. The paper analyzes the sensitivity to damage of the proposed methodology by considering some practical issues such as the size of the crack, the number of measuring points and the effect of experimental noise.
Highlights
It is well known that the presence of damage in a beam implies a change in its dynamic properties
The authors have recently proposed a simple damage detection technique based on the wavelet analysis of difference in mode shapes from a healthy and a damaged state [5]
This section analyzes the sensitivity to damage of the proposed methodology considering the effect of the number of measuring points, the presence of experimental noise and the use of the added roving mass
Summary
It is well known that the presence of damage (cracks) in a beam implies a change in its dynamic properties. The authors have recently proposed a simple damage detection technique based on the wavelet analysis of difference in mode shapes from a healthy and a damaged state [5]. The signal starts and finishes at those points, so there is a significant local change there, unless the signal trends softly in an asymptomatic way to a constant value, which will never be the case for a mode shape This unstable behavior of the wavelet coefficients in the vicinity of the beginning and the end of the analyzed signal is known as the edge effect, and it is a serious doi:10.1088/1742-6596/628/1/012014 drawback of the wavelet transform when the damage is close to the beginning or the end of the signal. In this paper a softening technique already proposed by the authors is applied [5].It is based on a windowed quadratic regression
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