Sound wave signal feature extraction method for micro leakage of gas pipeline based on random resonance

Abstract

The invention provides a sound wave signal feature extraction method for micro leakage of a gas pipeline based on random resonance. The method comprises the following steps that a discrete random resonance system model of a nonlinear filter is established, output signals of the random resonance system model are converted, windowing is carried out on the converted sound wave signals, the signals are converted to a Melfilter domain, discrete cosine transform is carried out to extract feature parameters, and denoising is carried out by utilizing a cepstrum mean-value normalization algorithm. According to the method, the random resonance system model is introduced into sound wave signal feature extraction in the Mel frequency cepstrum coefficient algorithm, and compared with an MFCC feature extraction algorithm, the signal to noise ratio is higher, identification is accelerated, and the method of the invention is more helpful for dynamic feature extraction.

Description

Technical field [0001] The invention relates to the field of gas pipeline safety detection, in particular to a method for extracting sound wave signal characteristics of gas pipeline micro-leakage based on stochastic resonance. Background technique [0002] With the rapid development of my country's economy, the demand for natural gas is increasing, and pipelines are the main way to transport natural gas on land. For this reason, the country has built a large number of natural gas pipelines. However, pipeline leakage occurs from time to time due to pipeline corrosion, natural damage, pipeline defects or third-party damage. Leakage in the pipeline not only brings economic losses and environmental damage, but also causes serious accidents such as deaths and injuries. Therefore, leakage detection and location methods are important means to ensure pipeline safety, which has important research significance. At present, most of the gas pipelines are inspected manually, which is less e...

AI Technical Summary

Problems solved by technology

[0005] This application provides a stochastic resonance-based method for extracting the sound wave signal characteristics of gas pipeline micro-leakage, and introduces the stochastic resonance principle into the sound wave signal feature extraction of the Mel frequency cepstral coefficient algorithm to solve the problems caused by random noise and noise in the prior art. Noise and other influences caused by various external disturbances make it impossible to accurately identify the leaked sound wave signal, resulting in false positives or false positives.

Images