输油管道超声信号的小波包奇异值分解去噪

Denoising of Wavelet Packet and Singular Value Decomposition for Oil Transportation Pipeline Ultrasonic Signal

采用超声法检测输油管道的内外壁缺陷时,缺陷回波信号中会夹杂扫描系统噪声和背景噪声,特别是在检测微小缺陷时,较低幅值的回波信号更容易淹没在噪声中。针对该问题,提出了一种基于小波包和奇异值分解(SVD)的去噪方法,回波信号进行小波包分解与利用最优小波函数,根据不同频带的能量分布率重新移动高频噪声,然后利用奇异值分解提取的能量分布带。最后,通过奇异熵分析挖掘有效回波信号区域,实现了滤波器的自动滤波功能。实验结果表明:基于小波包和奇异值分解的去噪算法不仅可以去除高频噪声,而且能有效地去除低频噪声,大幅提高了信噪比。

The defect echo signals are often suffered from scan system noise and background noise during detecting internal and external defects of oil transportation pipeline by ultrasonic testing method.Especially during detecting tiny defects,the lower amplitude echo signals are easily submerged in noise.In response to this issue,a new approach of denoising based on wavelet packet and singular value decomposition (SVD)is proposed.The echo signal is decomposed with wavelet packets by utilizing the optimal wavelet functions.The high frequency noise is removed according to the energy distribution ratio of different frequency band,and extracted energy distribution band is decomposed by singular value.The automatic filtering function for rejector is realized after determining the effective echo signal area through singular entropy analysis.Experimental result shows the denoising algorithm based on wavelet packet and singular value decomposition can not only remove the high frequency noise,but also can effectively remove low frequency noise.Echo signal-to-noise ratio (SNR)is improved greatly.