Pipeline leak acoustic emission signal processing method based on compressive sensing and mask signal method hht

Abstract

The invention relates to a pipeline leakage acoustic emission signal processing method based on compressed sensing and mask signal method HHT. It can be mainly used in the detection of leakage acoustic emission signals of pipelines and boilers. The main processing steps are as follows: the first step: obtain the original acoustic emission signal, and use a digital filter to filter out the high-frequency noise beyond the frequency domain of the leakage acoustic emission signal; the second step : Introduce compressive sensing theory to compress and sample the acoustic emission signal; the third step: use the OMP algorithm to accurately reconstruct the compressed signal; the fourth step: use the EMD mask signal method to decompose the acoustic emission signal, and the different frequencies in the signal The components are separated one by one from high frequency to low frequency; the fifth step: Hilbert transform is performed on each frequency component of the acoustic emission signal to determine the start and end time of the acoustic emission signal.

Description

technical field [0001] The invention relates to a pipeline leakage acoustic emission signal processing method based on compressed sensing and mask signal method HHT. Background technique [0002] The signal acquisition method based on the traditional Nyqusit sampling theorem requires that the sampling frequency be at least twice the highest frequency of the signal. The frequency range of the acoustic emission signal is several thousand Hz to several hundred thousand Hz. In practice, a multi-channel data high-speed acquisition system is generally used, and the sampling frequency is 5 to 10 times the highest frequency of the signal. When monitoring the acoustic emission signal in real time, it will inevitably generate The huge amount of data makes the cost of hardware implementation relatively high, which puts huge pressure on subsequent data transmission, storage and processing, which is very unfavorable for the long-term monitoring of leakage signals. [0003] The acoustic ...

AI Technical Summary

Problems solved by technology

However, when the signal to be decomposed has multiple similar frequencies, the traditional EMD decomposition method will have mode aliasing

The EEMD method proposed later continuously adds white noise during the signal decomposition process, so that the additional white noise is evenly distributed in the entire time-frequency space, and then the signal is independently tested, and the noise will be eliminated by using the overall mean value of enough tests. EEMD can solve the modal mixing problem in EMD to a certain extent, but it still cannot achieve the ideal detection effect when the signal amplitude-frequency ratio does not meet the requirements

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