Pipeline leakage feature vector extraction method based on improved wavelet packet

Abstract

The invention provides a pipeline leakage feature vector extraction method based on an improved wavelet packet and relates to the technical field of signal feature extraction. The method comprises the first step, the second step and the third step of acquiring a signal and conducting denoising on the signal, the fourth step of adopting an improved wavelet packet algorithm to reconstruct a single-band signal, and the last two steps of extracting a feature vector. In order to overcome the shortcomings of a traditional wavelet packet algorithm, the improved wavelet packet algorithm is disclosed, and a signal prolongation mode and a signal processing mode after convolution are combined to improve the wavelet packet algorithm. A parabola prolongation mode is adopted in the improved algorithm, and FFT and IFFT are adopted in the signal processing mode after convolution. Test results show that the phenomenon of redundant frequency components generated during reconstruction of the single-band signal can be overcome by the improved algorithm, a leakage feature signal is accurately reconstructed, and the feature vector is accurately extracted. The pipeline leakage feature vector extraction method greatly increases detection accuracy, reduces the false alarm rate and also lays a foundation for improving follow-up locating precision.

Description

technical field [0001] The invention relates to the technical field of signal feature extraction, in particular to a pipeline leakage feature vector extraction method based on improved wavelet packets. Background technique [0002] In petroleum, chemical, natural gas and other industries, gas pipeline leakage seriously affects the normal production, pollutes the environment, and even brings safety accidents. [0003] Acoustic emission, also known as stress wave emission, refers to a common physical phenomenon in which materials emit transient elastic waves locally due to the rapid release of energy. Most materials have acoustic emission when they deform and break. Acoustic emission detection is an important non-destructive detection method for pipeline leakage, which has high detection speed, high sensitivity and low work intensity. Acoustic emission detection of pipeline leaks needs to extract some characteristic parameters in the time-frequency domain from the acoustic em...

AI Technical Summary

Problems solved by technology

However, due to the non-ideal characteristics of the wavelet filter and the spectrum expansion and compression brought about by upsampling and downsampling, the reconstructed single subband signal will be mixed with redundant frequency components, and the eigenvectors extracted from the single subband signal are not Inaccurate, inputting such eigenvectors into the decision function will cause false alarms, greatly reducing the accuracy of leak detection, and will also result in low accuracy of subsequent leak location

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