Operation feature extraction of flood discharge structures under ambient excitation has attracted increasing attention in recent years. However, the vibration signal of flood discharge structures is a nonstationary random signal with low signal-to-noise ratio, which is mixed with lots of low-frequency water flow noise and high-frequency white noise. It is difficult to excavate the hidden vibration characteristic information accurately. To solve the problem, we propose a novel denoising method called improved variational mode decomposition. As an improved method of variational mode decomposition, improved variational mode decomposition can effectively determine the decomposition mode number of variational mode decomposition by using the mutual information method. Furthermore, improved variational mode decomposition is combined with a variance dedication rate to extract the overall operation characteristic information of the structure. In order to evaluate the applicability and effectiveness of the proposed improved variational mode decomposition–variance dedication rate method, we compare the denoising results of simulation signals produced by an improved variational mode decomposition–variance dedication rate with those produced by digital filter, wavelet threshold, empirical mode decomposition, empirical wavelet transform, complete ensemble empirical mode decomposition with adaptive noise, and improved variational mode decomposition methods and find a better performance of the improved variational mode decomposition–variance dedication rate method. In addition, the proposed method is applied to the Three Gorges Dam, and the results show that the improved variational mode decomposition–variance dedication rate method can effectively remove heavy background noises and extract the operation characteristic information of the flood discharge structure, which contributes to health monitoring and damage identification of the flood discharge structure.
Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means
