Research on detection and recognition methods of gas pipelines based on acoustic signal feature analysis

Abstract

In the process of reconstruction and expansion of gas pipeline, it is easy to destroy in-service gas pipeline and cause safety accidents. In order to realize the detection of in-service pipelines, based on the characteristics of low sound pressure level and easy attenuation of acoustic signals of gas pipelines, the detection and identification method of gas pipelines based on acoustic signal feature analysis was studied by using Hilebert–Huang transform algorithm and optimized Back Propagation (BP) neural network. This method takes the gas pipeline flow noise signals obtained by numerical simulation and experimental verification as the research object, and the underwater acoustic signals are collected for comparative analysis. Empirical Mode Decomposition (EMD) was used to decompose the two signals, and the time-domain waveform of Intrinsic Mode Functions (IMF) component was obtained, and the characteristic parameters of peak value and peak frequency were determined. The energy characteristic parameters of Hilbert marginal spectrum were calculated, and the characteristic database of gas pipeline flow noise signal was obtained. The optimized BP neural network was used for pattern recognition. The results show that the identification rate of gas pipeline acoustic signal can reach 97.5% by using this method, which verifies the effectiveness of the gas pipeline detection and identification method in this paper.

VMD analysis of pulsating flow pressure signal of oil with different particle concentration

“…At present, the analysis methods of non-stationary signals, such as short-time Fourier transform (STFT), 11 Hilbert-Huang transform, 12 and analytical mode decomposition, 13 have their different problems. The resolution of the time-frequency of STFT is limited by the uncertainty principle; the Hilbert-Huang transform has fuzzy time-frequency distribution such as endpoint effect and mode aliasing, analytical mode decomposition is only suitable for analyzing stationary frequency multicomponent signals.…”

Anti-interference detection and operation state identification of transformer acoustic characteristics based on Conv-Tas-ResNet

“…As opposed to the present traditional detection methods which have the issue of limited effect on background sound suppression, the speech noise reduction algorithms based on deep learning carry outstanding advantages and have been able to reach better noise reduction effects in scenes such as conference calls [8] , cockpits, and factory floors [9] . Moreover, it has been applied in a variety of fields, including gas pipeline detection [10] , Covid-19 prevention and control [11] , pig behaviours feature recognition [12] , power equipment discharge identification [13][14] , and others.…”