Defects localization using the data fusion of laser Doppler and image correlation vibration measurements

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This work proposes a defects localization method based on the combination of laser Doppler vibrometry (LDV) and Digital image correlation (DIC). DIC and LDV are the dominant non-contact vibration measurement methods with a matured theory and high stability. DIC gives a high spatial resolution with a measurement accuracy of several microns, whereas the Multipoint LDV has a limited spatial resolution with high-precision results for the sparse points up to nanometer level. However, the vibration-based defect localization (VBDL) necessitates certain measurement methods with both the high spatial resolution and measurement accuracy. Unfortunately, the accuracy of DIC is not sufficient to detect the data anomalies caused by the structural defects. Nevertheless, LDV cannot achieve a high-precision defect localization owing to the limited spatial resolution. In this study, a nonlinear weighted least-squares fitting has been proposed to improve the measurement accuracy and reliability. A Chebyshev polynomial has been employed as the basis function in the fitting process. The effective signal-to-noise ratio (SNR) and the correlation coefficient are employed as the weight basis of the LDV points and the DIC subsets, respectively. Based on the simulation analysis, the determination of the optimal polynomial order at multiple noise levels and the influence of the LDV points distribution are discussed. The experiment proves that the data fusion method can significantly improve spatial resolution and measurement accuracy. From the data fusion results, the reference-free defects localization in cantilever beams and plates based on residuals have been presented.