Leakage acoustic wave feature extraction method based on fusion of wavelet transform and blind source separation algorithm

Abstract

The invention discloses a leakage acoustic wave feature extraction method based on fusion of wavelet transform and the blind source separation algorithm. The method comprises the following steps that an acoustic wave sensor is used for collecting leakage acoustic wave signals and acquiring leakage acoustic wave collection signals; multilayer wavelet decomposition is carried out on the leakage acoustic wave collection signals through wavelet transform, corresponding approximate signals are sequentially obtained through all layers of wavelet decomposition, the leakage acoustic wave collection signals and the approximate signals are used as observation signals, and the observation signals are processed through the blind source separation algorithm to obtain target signals; and the target signals obtained in the second step are evaluated, and compositions of the observation signals are optimized. The method has the beneficial effects that the target signals are evaluated through two evaluation parameters, namely leakage moment sampling point deviation and amplitude loss; by means of the method, the leakage moment can be accurately determined, and meanwhile the compensation effect on the leakage amplitude of weak signals is obvious.

Description

technical field [0001] The invention relates to the field of oil and gas pipeline acoustic leakage monitoring, in particular to a leakage acoustic wave feature extraction method based on wavelet transform fusion blind source separation algorithm. Background technique [0002] At present, 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; the measurement 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, the detection range is wider, etc. [0003] When a gas pipeline leaks, an acoustic signal is generated, and as the propagation distance i...

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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