Ultrasonic Defect Echoes Identification Based on EEMD and Low-Rank Sparse Decomposition
ZHOU Hang-rui1, SUN Jian1, XU Hong-wei1, MIAO Cun-jian2, SONG Xin2
1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. Zhejiang Provincial Special Equipment Inspection And Research Institute, Hangzhou, Zhejiang 310012, China
Abstract：In order to detect the minor defect echoes of metal materials from noisy signal in ultrasonic nondestructive testing, a ultrasonic defect echoes identification method based on ensemble empirical mode decomposition (EEMD) and low-rank sparse decomposition was proposed. First, the EEMD was performed on the defect detection signal to obtain a series of intrinsic mode functions (IMF). The similarity measurement method based on probability density function was used to select irrelevant IMFs and these IMFs were discarded to achieve preliminary noise reduction. Then a denoising method based on short-time Fourier transform (STFT) and low-rank sparse decomposition algorithm was used for further noise suppression of the reconstructed signal. Finally, the inverse STFT was performed to obtain denoised defect echo signal in time domain. The simulated and measured signals are processed separately, and the results show that the method is effective in defect echo detection.
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2022, Vol. 43 
