Research on Noise Reduction Processing of Magnetostrictive Guided Wave Detection Signal

In order to solve the noise interference in the detection process of magnetostrictive guided wave, this paper proposes a noise reduction algorithm based on optimized mixed mode decomposition for guided wave detection signal. The method uses empirical mode decomposition (EMD) and calculates the correlation coefficient of the decomposition mode and compares it with the correlation coefficient threshold to obtain an effective signal. The effective signal reconstruction achieves the purpose of eliminating the low-frequency noise of the guided wave detection signal and obtains the pre-processed signal. By designing Particle Swarm Optimization (PSO), the sample entropy (SampEn) is used as the fitness function to realize the adaptive optimization of the optimal mode number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> and the quadratic penalty factor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> of VMD. The preprocessed signal is decomposed by the optimized variational mode. Combined with the correlation coefficient index, the decomposed mode is divided into effective mode and noise mode. The noise mode is denoised by wavelet threshold and reconstructed with effective mode to achieve the purpose of eliminating high-frequency noise. The performance of EMD, VMD and wavelet soft threshold de-noising algorithms in noise reduction of guided wave detection signals is compared. It is concluded that the optimized mixed mode decomposition method has better signal-to-noise ratio than other noise reduction methods in the noise reduction of magnetostrictive guided wave signals, and can better restore the details of the original signal.