Application of SSA noise reduction algorithm in ultrasonic testing

Some noise signals are often carried in ultrasonic detection signals, and the most of them are the scattering noise at material grain boundary and system noise. In view of the limitations or shortcomings of some traditional methods of ultrasonic signal noise reduction, this paper introduces the singular spectrum analysis (SSA) algorithm to the noise reduction of ultrasonic signals. The method originates from principal component analysis (PCA). The main component of signal was extracted according to the difference of singular value between the main component and noise component in singular spectrum, and then several extracted signal principal components were reconstructed to realize the purpose of noise reduction. The noise reduction effect of SSA algorithm is compared with traditional methods such as wavelet threshold denoising, EMD filtering and sparse decomposition reconstruction. The experimental results show that SSA algorithm has better noise reduction effect on different SNR signals, which was significantly better than other traditional noise reduction methods, and no more prior information was needed.