基于SARM的管道声波信号降噪方法

A novel method for noise reduction of pipeline acoustic signals based on SARM

天然气管道声波信号中的噪声容易干扰管道泄漏识别检测,因而提出了一种均值近似值移除方法(Small Approximations Removal by Means,SARM)去除声波信号中的噪声。SARM以低频小波重构系数为信号均值线,继承了小波分析(Wavelet Analysis,WA)良好的时频分辨特点,将信号均值线附近的小波动去除,使曲线光滑的同时又很好地保留信号突变特征,获得良好的降噪效果。实例分析表明:相比小波包(Wavelet Packet,WP)、WA及奇异值分解(Singular Value Decomposition,SVD)方法,SARM具有更好的降噪效果,为天然气管道的泄漏检测与定位提供了一定的技术支持。

Pipeline leak identification and detection is usually interfered by the noise contained in acoustic signals of natural gas pipelines. In this paper, therefore, a novel noise reduction method called Small Approximations Removal by Means （SARM） was proposed to remove the noise from acoustic signals. In the SARM method, the reconstruction coefficient of the low-frequency wavelets is taken as the mean line, so the good time-frequency resolution performance of wavelet analysis （WA） is inherited. And the small fluctuations around the mean line are removed. In this way, the curve is very smooth while the mutation characteristics are well preserved, indicating good noise reduction effect. Based on the case study, SARM is superior to Wavelet Packet （WP）, WA and Singular Value Decomposition （SVD） in terms of noise reduction effect. It provides technical support for leak detection and location of natural gas pipelines.