Application of soft-thresholding on the decomposed Lamb wave signals for damage detection of plate-like structures

Effective application of the Lamb waves for structural health monitoring and damage identification intensively relies on the accurate damage-related feature extraction in the received signals. Most of existing signal processing methods extract the damage-related features from the time–frequency joint spectrum which requires a quite amount of effort. In this paper, the soft-thresholding process, based on different signal decomposition methods, is introduced to damage identification so that the damage-related signal features can be manifested more distinctively. By applying two popular signal decomposition methods (i.e., the discrete wavelet transform (DWT) and the empirical mode decomposition (EMD)), the signal of interest can be represented by a series of components with different frequencies. Since most noises exist in the high frequency range, it is feasible to alleviate noise by restricting the energy of high-frequency components. Finally, a denoised signal is synthesized using the corresponding reconstruction method. As an application, the soft-thresholding process is performed to detect a small crack on an isotropic aluminum plate under the white Gaussian noise contamination. The results, from both the numerical finite element simulation and experimental test, indicate that the soft-thresholding process is capable of effectively reducing the effect of noise, convincingly improving the sensitivity of damage identification, and discriminating relatively small damage.