Comparisons of wavelets and contourlets for vibration-based damage identification in the plate structures

Abstract

The objective of the present study is to compare a new approach based on contourlet transform and a traditional method based on wavelet transform to demonstrate curve damages in plate structures. The contourlet transform approach, as a novel two-dimensional development of wavelet transform, was extended to deal with inherent restrictions of wavelets. According to previous studies, wavelets have indicated poor performance to detect curve damages due to its basic elements. Therefore, this study utilized contourlet transform as a new method having an efficient performance to display this kind of discontinuities. In this research, contourlet transform and wavelet transform have been applied to a plate using four fixed boundary conditions including circle damages with arbitrary specifications and location in order to demonstrate their performance in damage identification. Comparing damage shape attained from contourlet transform and wavelet transform, it was revealed that contourlet transform could be utilized as an accessible and useful technique to detect damage with curvature.

Assessment of Internal Damage in Sandwich Structures by Post-Processing of Mode Shapes Using Curvelet Transform

“…There were also a few applications of X-lets in damage identification problems. Bagheri et al [ 44 , 45 ] used CT for identification of simulated structural damage in plates using their mode shapes, De Marchi et al [ 46 ] used curvelets in their structural health monitoring approach for improvement of a damage detectability in a guided wave-based technique, while the recent studies of Vafaie and Salajegheh [ 47 ] proposed an application of wavelets and contourlets for vibration-based damage identification in plates.…”

“…Moreover, the variety of transforms within the XTs family raises the question of a suitable transform for damage identification. The previous comparative studies on the performance of wavelets vs. X-lets [ 47 , 48 ] clearly show significant improvement of filtering ability and sensitivity to damage of the latter ones, while the comparisons of the performance of X-lets between each other [ 44 , 47 , 48 ] indicates that curvelets provide the best filtering performance, which makes them suitable for improvement of sensitivity to damage in damage identification problems.…”