Denoising Method of Pipeline Leakage Signal Based on VMD and Hilbert Transform

Abstract

In order to solve the problem of low localization accuracy of water supply pipeline leakage detection using acoustic emission signals, a noise reduction algorithm based on variational mode decomposition (VMD) and Hilbert transform is proposed in this paper. Firstly, the leakage signal is decomposed into several intrinsic mode functions (IMF) using VMD, and the number of decomposition layers is determined, and the IMF component is transformed by Hilbert to get the marginal spectrum, and the noise are preliminarily filtered according to the marginal spectrum characteristics and the cross-correlation coefficient. Secondly, the signal denoising is realized by using filtering technology and cross-correlation coefficient. Finally, the time delay estimation of the denoised signal is estimated and the location of the leakage point is calculated based on the positioning principle. In order to verify the performance of the algorithm, a water supply pipeline experiment platform for leakage simulation and leak location experiment was conducted. The cross-correlation algorithm, VMD-correlation coefficient algorithm, and the proposed algorithm are analyzed through simulation and experiment. The results show that compared with the other two algorithms, the proposed algorithm can effectively remove the noise in the leakage signal and improve the accuracy of leakage location.

Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation*

“…Under laboratory conditions, however, it can be difficult to achieve the conditions of cracks, and there may be security risks. By consulting the relevant literature, many scholars have simulated leakage in the form of holes, such as in [12]. Therefore, in this experiment, the leakage point was simulated in the form of holes, and the diameter of the leak hole was 4 mm, as shown in figure 6(d).…”

“…Jiang et al proposed a noise reduction algorithm based on variational mode decomposition (VMD) and Hilbert transform. Their results showed that the proposed algorithm effectively removed the noise in the leakage signal and improved the accuracy of the leakage location [12]. VMD, unlike EMD's cyclical sifting and stripping, is based on robust mathematical derivation, separating each component of the signal by iteratively searching for the optimal solution of a constrained variational model [13].…”