Vibration-based nondestructive damage detection relying on modal curvatures has been investigated in various applications. An intrinsic deficiency of a modal curvature is its susceptibility to the noise inevitably present in a measured mode shape. This adverse effect of noise is likely to mask actual features of damage, resulting in false results of damage detection. To circumvent this deficiency, a Teager energy operator (TEO), aided by a wavelet transform, is adopted for the treatment of mode shapes to produce a new algorithm for damage identification. After wavelet-transform- based preprocessing to separate the effective components of modal curvatures from noise interference, a TEO is employed as a singularity detector, acting on the separated effective components, to reveal and characterize the features of damage. The capacity of the TEO is demonstrated analytically in cases of cracked beams. The applicability of the algorithm is experimentally validated using a scanning laser vibrometer to acquire mode shapes of an aluminum beam bearing a crack. The analytical and experimental results show that the TEO, aided by wavelet transforms, has stronger sensitivity to slight damage and greater robustness to noise than modal-curvature- and wavelet-transform-based damage detection methods.

DOI: 10.1117/12.2009425