Leakage Acoustic Feature Extraction Method Based on Wavelet Transform Fusion Blind Source Separation Algorithm

Abstract

The invention discloses a leakage acoustic wave feature extraction method based on wavelet transform fusion blind source separation, comprising the following steps: using an acoustic wave sensor to collect leakage acoustic wave signals to obtain the leakage acoustic wave acquisition signal; using wavelet transform to perform multi-layer wavelet on the leakage acoustic wave acquisition signal Decompose each layer of wavelet decomposition to obtain the corresponding approximate signal in turn, use the leakage acoustic wave acquisition signal and the approximate signal as the observation signal, and use the blind source separation algorithm to process the observation signal to obtain the target signal; for the target in step 2 The signal is evaluated and the composition of the observed signal is optimized. The beneficial effects of the present invention are: the present invention evaluates the target signal through the two evaluation parameters of sampling point deviation and amplitude loss at the leakage time, the method can accurately locate the leakage time, and at the same time compensate the leakage amplitude of the weak signal obvious.

Description

Technical field [0001] The invention relates to the field of oil and gas pipeline acoustic wave leakage monitoring, in particular to a leakage acoustic wave feature extraction method based on wavelet transform fusion blind source separation algorithm. Background technique [0002] There are many leakage monitoring methods that can be applied to oil and gas pipelines. Among them, the acoustic wave method has many advantages compared with the traditional mass balance method, negative pressure wave method, and transient model method: high sensitivity, high positioning accuracy, and false alarms. Low rate, short detection time, strong adaptability; what is measured is the weak dynamic pressure change in the pipeline fluid, which has nothing to do with the absolute value of the pipeline operating pressure; the response frequency is wider, and the detection range is wider. [0003] When the gas pipeline leaks, the acoustic signal is generated. As the propagation distance increases, the l...

AI Technical Summary

Problems solved by technology

[0009] (1) Wavelet transform can extract low-frequency signal features, which is the most widely used signal processing method, but at the same time, wavelet transform also has obvious defects in signal extraction. In practical applications, the acquisition of low-frequency signal features requires When the original signal is decomposed in depth, it is easy to have a large deviation in the location of the leakage time and the acquisition of the leakage amplitude, which is likely to cause a calculation error of the time difference, resulting in a large positioning error; the loss of the leakage amplitude is likely to cause distortion of the leakage waveform, which is easy to cause Missed and misjudged leaks

[0010] (2) In order to solve this problem, the blind source separation algorithm is used to process the signal. After research, it is found that blind source separation can accurately locate the leakage time, and there is no loss in the leakage amplitude, but it is compensated, especially in the signal The compensation is more obvious when it is relatively weak, but in application, blind source separation also has obvious defects: first, the similarity of the waveform characteristics obtained by processing becomes worse, and the amplitude change is irregular; second, the target signal obtained by blind source separation The order and type cannot be determined

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