The paper aims to detect the non-stationary of fouling data and modal aliasing which may make it difficult to realize dirt characteristic separation of ultrasonic time-domain. As for deficiencies such as insufficient effective information loss caused by directly application of power spectral density in determination of CEEMD decomposition of noise interval as well as deformation of reconstructed signal caused by traditional wavelet denoising, self-adaptive soft threshold noise reduction of autocorrelation function based on CEEMD as well as modal correlated characteristic curve are introduced to determine the IMF component with higher noise contribution. Besides, the method of wavelet self-adaptive soft-threshold value is also applied to collect useful high-frequency signal in noise component. According to the results of simulated analysis and experimental research, self-adaptive noise reduction method based on CEEMD and autocorrelation is more effective than traditional wavelet threshold and pute CEEMD. It can better solve the problem of modal aliasing and extract dirt characteristic signal, which is of great importance to the processing of ultrasonic detection signal.
孙灵芳1，徐曼菲2,朴亨2，李霞2. 基于改进CEEMD的超声检测信号自适应降噪[J]. 振动与冲击, 2017, 36(20): 225-232.
SUN Ling-fang1 XU Man-fei2 PIAO Heng2 LI Xia2. Self-adaptive Noise Denoising for Ultrasonic Detection Signal based on Improved CEEMD. JOURNAL OF VIBRATION AND SHOCK, 2017, 36(20): 225-232.
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