An Improved Wavelet Threshold Denoising Algorithm based on CEEMDAN-BA

In order to solve the problem that porcelain insulators and porcelain bushings are easily disturbed by environmental noise in ultrasonic detection and improve the accuracy of porcelain insulator defect identification, a complete ensemble empirical mode based on adaptive noise is proposed decomposition with adaptive noise (CEEMDAN) -Bat Algorithm (BA) improves the wavelet threshold ultrasonic denoising method. Firstly, a series of intrinsic mode function (IMF) from high frequency to low frequency is obtained by CEEMDAN decomposition of the original signal to be measured. Then, the correlation coefficient is calculated to determine the noisy IMF component of the high-frequency IMF signal, and the discrete stationary wavelet transform (SWT) is used to decompose the noisy IMF signal, and the approximate coefficient and detail coefficient are obtained. Then, the minimum mean-square error (MSE) is taken as the objective function, and the optimal threshold and variable coefficient are obtained by BA algorithm. Finally, the denoised ultrasonic signal is obtained by reconstructing the denoised component and the remaining component. The simulation results all show that the proposed denoising algorithm can greatly improve the signal-to-noise ratio of the signal, and retain the effective information in the original ultrasonic signal, which is of great significance for the accuracy of the location of defects such as insulator micro-cracks by using ultrasonic signals in the future.