SG-VMD-SVD的信号去噪方法研究

油气管道信号泄漏检测易受噪声影响,因此去噪成了关键问题.为了提高对油气管道信号的去噪效果,提出了一种基于Savitzky-Golay平滑滤波、变分模态分解(VMD: Variational Mode Decomposition)和频域奇异值分解(SVD:Singular Value Decomposition)去噪相结合的油气管道信号的联合去噪方法.首先,针对泄漏信号在时域利用SG平滑滤波降噪,去除尖脉冲、高频成分等噪声,提高输入信号的信噪比;将滤波后的信号利用VMD分解,通过计算各个本征模态分量(IMF: Intrinsic Mode Function)与信号之间的曼哈顿距离,从而区分信号分量与噪声分量,对噪声分量进行频域奇异值(SVD)去噪,最后将滤波后的分量与信号分量进行重构,得到最终降噪后的信号.通过仿真和实际实验表明,该方法与单一VMD法、VMD-小波变换、SG-VMD-时域SVD去噪方法相比,去噪后所得信号信噪比相对较高,并验证了该方法去噪效果的优越性和对油气管道泄漏信号去噪的可行性.

Study on Signal De-Noising Method of SG-VMD-SVD

Signal leakage detection of oil and gas pipelines is easily affected by noise, so de-noising becomes the key point. In order to improve the de-noising effect of oil and gas pipeline signals, a combined de-noising method based on savitzky-Golay smoothing filter, variational mode decomposition and frequency domain singular value de-noising method is proposed. Firstly, savitzky-Golay smoothing filter is used to reduce the noise of the leakage signal in the time domain to remove the sharp pulse, high-frequency components and other noises and to improve the signal-to-noise ratio of the input signal. By calculating the Intrinsic Mode components (IMF: Intrinsic Mode Function) and the Manhattan distance between signals, distinguishing the signal components and noise, the noise component frequency-domain SVD (Singular Value De-noising), finally the filter components and signal are constructed. Simulation and practical experiments show that compared with single VMD method, VMD- wavelet transform and SG-VMD-SVD method, the signal to noise ratio is relatively higher, which verifies the superiority of de-noising effect and the feasibility of de-noising oil and gas pipeline leakage signal.