CEEMDAN–WTD denoising method for enhanced magnetic memory detection signal of defects

During the enhanced magnetic memory detection of defects, many interference signals appear in the detection signal, which makes it difficult to accurately extract the characteristics of the defect signals and significantly affects the detection effectiveness. When the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed independently for signal denoising, the noise and feature signals of the transition components will be retained or removed. When wavelet threshold denoising (WTD) is employed independently for signal denoising, the denoising effect is restricted because of the difficulty in determining the number of decomposition layers <i>m</i> and wavelet basis function. To solve these problems, a denoising method for enhanced magnetic memory detection signals based on CEEMDAN and WTD, called CEEMDAN–WTD, is proposed in the paper. Firstly, the correlation coefficient R of the signal components obtained by performing decomposition with the CEEMDAN method is calculated, and the signal components are divided into noise-dominant components, transition components, and useful signal components based on the R. Subsequently, WTD is employed to perform denoising on the two order signal components before and after transition components <i>IMF_(k&minus;2) ~ IMF_(k+2)</i> obtained from the CEEMDAN method. Ultimately, the denoising components <i>IMF_(k&minus;2) ~ IMF_(k+2)</i> and high-order useful signal components obtained by the CEEMDAN method are selected for signal reconstruction to obtain a denoised signal. To validate the effectiveness of the proposed method, the denoising effects of the CEEMDAN–WTD, CEEMDAN, and WTD methods were compared based on the signal-to-noise ratio (SNR). The comparison indicated that the CEEMDAN–WTD denoising method significantly enhanced the denoising effect, and the SNRs of the components of the magnetic field signal could be increased by up to 8.24% and 45.88%, respectively, indicating that the CEEMDANWTD denoising method is relatively more effective.