Continuous wave damage identification method based on compressive sensing and machine learning

Vollständiger Abstract

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Although the guided wave testing is a common method for the non-destructive testing of plates, continuous wave excitation is rarely employed in practical applications. Therefore, this study proposes a damage localization method that integrates compressive sensing and continuous wave excitation, which achieves damage location prediction via the two-dimensional fast Fourier transform (2D-FFT) technique. First, sparse laser-acquired signals are reconstructed into a full wavefield by using a group sparsity-aware convolutional neural network, thereby ensuring compliance with the sampling requirements for 2D-FFT. When continuous waves are excited, primarily one mode of waves is excited in the plate, which manifests as a single peak in the frequency-wavenumber spectrum. Notably, the amplitude of this peak correlates directly with the damage location. Leveraging this characteristic, the damage location can be calculated by identifying the lines with the maximum amplitude from randomly selected reference points and determining the centroid of their intersection. However, for damage outside the measurement region, deviations arise between the actual and predicted locations. A deep forest model thus adjusts localization using the relationship between the amplitude of slices and the tangent of the line passing through the damage. Owing to the steady-state nature of continuous wave excitation, the proposed method extracts frequency-wavenumber features that are inherently more robust to measurement noise than conventional transient-excitation approaches. Both simulated and experimental results of steel plates demonstrate that the proposed method can precisely locate damage localizations both within and outside the measurement region.