This paper presents a new approach for damage detection in thin plates by fusing variational mode decomposition and spectral entropy (VMD-SE). In this method, after the received signal is decomposed into some intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the spectral entropy ratio of the first and last IMFs is calculated for optimizing the VMD’s parameters and improving its decomposition performance. Moreover, the cross-correlation coefficient between the decomposed IMFs and the reference signal is computed to separate the desired IMF, which contains more damage information. Finally, the spectral entropy of the obtained IMF is calculated as an indicator for assessing the damage’s severity. The comparative analysis of the simulated signal clearly shows that only the proposed method can successfully separate the damage-related and reference signals. To verify the VMD-SE method, damage detection of two different types of damage on aluminum and composite fiber-reinforced polymer (CFRP) plates is conducted by using this new approach. The experimental results demonstrate that the parameters of VMD affect greatly its decomposition performance, and the best parameters are selected. The results also indicate that the normalized spectral entropy monotonically increases when the diameter of the through-hole or the length of the scratch increases. In addition, the correlation coefficients of the fitting lines of the plates are larger than 0.998. The experimental results of aluminum specimens demonstrate that the damage’s location has an influence on the normalized spectral entropy. At last, based on the linear relationship, the severity of damage in the fourth specimen is identified. The identification results demonstrate that the relative error of the aluminum and CFRP plates is less than 7.34%, which indicates that this new algorithm by fusing VMD and spectral entropy can detect the damage size in thin plates accurately and efficiently.
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