# Research on Leakage Location of Pipeline Based on Module Maximum Denoising
###### Abstract
The time difference between the two negative pressure wave signals can be used to determine the leakage location [10]. The method based on NPW signal has the advantages of low price, easy operation, long transmission distance, and high stability accuracy. So, the research in this paper will be based on the negative pressure wave theory. The sensor will inevitably mix some noise in the process of NPW signal acquisition. The noise will reduce the Signal-to-noise ratio (SNR) of the detected signal, and even drown the useful signal in serious cases, which will lead to locating the leak extremely difficult. Therefore, it is very necessary to denoise the signals collected by the sensor to reduce or eliminate noise and extract useful signals. Because wavelet transform has good time-frequency characteristics, different frequency components of signal can be decomposed by the wavelet transform, which has been widely used in signal denoising. The principle of wavelet threshold denoising is select the appropriate wavelet basis function and the number of decomposition layers to achieve adaptive signal decomposition, and then reconstruct the signal according to the threshold (hard threshold or soft threshold) function. The hard threshold function is superior to the soft threshold method in the sense of mean squared error, but the signal generates additional oscillations and jump points, and this discontinuity leads to the pseudo-Gibbs phenomenon in the reconstructed signal. The wavelet coefficient estimated by the soft threshold function has good overall continuity, and the estimated signal does not easily produce additional oscillations, but there is a large deviation between the estimated value and the actual value [11; 12]. Variational mode decomposition (VMD) is an adaptive signal decomposition method proposed by [PERSON], in 2014 [13; 14]. Compared with wavelet decomposition, VMD can effectively suppress the phenomenon of modal aliasing and over-decomposition, and while it does not rely on intrinsic basis functions, it can adaptively decompose the signal into multiple intrinsic mode functions (IMF), from high to low frequency, and can achieve signal noise reduction by screening effective component reconstruction. However, there are still some limitations to the use of VMD. Before VMD decomposition of the signal, the number of modes K and the quadratic penalty term \(\alpha\) should be set in advance. In many cases, prior knowledge of the signal is unknown. If K and \(\alpha\) are not chosen properly, too few or too many modes will be generated, resulting in signal component loss or more noise. The module maximum method is a classical denoising algorithm, the module maximum amplitude of noise decreases rapidly with the increase of the scale, and the normal signal increases with the increase of the scale, so the noise can be removed from the normal signal. However, the signal after noise reduction has glitches and slight oscillations. In order to realize effective denoising of the actual signal, the advantages of the above methods are combined, and an improved module maximum denoising algorithm is proposed in this paper. Firstly, a discrete binary wavelet transform is used to find out the module maximum, corresponding to the wavelet transform coefficient on scale \(J\). Different threshold values are used to deal with the maxima of different scale modes, which can avoid the loss of signal components and the introduction of new noise. Finally, the NPW theory is used to locate the leakage point. Experimental results show that this method effectively improves the lack of adaptability of the traditional methods, and it can acquire a clear NPW inflection point. By using the same data, the proposed method can obtain the highest SNR. Thus, the noise suppression of our method is more effective. The structure of this paper is as follows: In Section 1, the research background of this paper, and some commonly used pipeline leakage detection methods and negative pressure wave signal denoising methods are introduced.