Abstract
Natural gas produced in the Russian Federation is transported through main gas pipelines united into the Unified Gas Supply System of the Russian Federation. The system of main gas pipelines - the most important link in the unified gas supply system – is a large, complex and continuously developing technological system. Maximum internal pressure is one of the key characteristics for any pipeline. This indicator helps to set the limit for the capacity of the pipeline (maximum volume of pumped gas per unit of time), its level of reliability, as well as the level of hazard and potential risk (the higher the pressure inside the pipeline, the more potential threat it carries). In order to increase the reliability of determining the pressure in the gas pipeline, it is proposed to perform noise suppression using a wavelet transform in the work. Noise reduction in the wavelet transform is carried out due to the fact that the signal is decomposed into approximating and detailing coefficients. After removing the detailing component, the decomposition is restored and the output is a slightly distorted signal. Thus, when you remove an insignificant part of the original signal, the graphs of the change in values become more visual. This paper compares the efficiency of wavelet based thresholding techniques in the presence of noise for various wavelet family. For comparison, the trend processing was performed by two types of wavelets recommended for noise reduction - Symlet and Daubechies wavelets.
Highlights
When passing through the communication channel, a noise component is added to any signal
Wavelet transforms are divided into two groups: discrete wavelet transform (DWT) and continuous wavelet transform (CWT)
A signal denoising method based on the wavelet tranformation has been proposed in order to improve the accuracy of measuring the pressure in the gas pipeline and noise suppression of the pressure sensor signal
Summary
When passing through the communication channel, a noise component is added to any signal. Various noise reduction methods are applied to signals, including those based on mathematical transformation. Wavelet spectrograms are more informative than conventional Fourier spectrograms, and unlike the latter, they allow revealing the finest local features of acoustic signals. This mathematical transformation is widely used as a signal processing and analysis method. It is performed in the same way, the signal is multiplied with the wavelet in the same way as with the Short Time Fourier. The wavelet transform allows the use of large time intervals where more accurate information about the low frequency is needed, and shorter ones when information about the high frequency is needed
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