Pipeline leakage acoustic emission signal processing method based on compressed sensing and HHT in mask signal method

Abstract

The invention relates to a pipeline leakage acoustic emission signal processing method based on compressed sensing and HHT in a mask signal method. The method can be mainly applied to leakage acoustic emission signal detection of pipelines and boilers. The method mainly includes the processing steps that firstly, acoustic emission original signals are obtained, and high-frequency noise exceeding a leakage acoustic emission signal frequency domain is filtered out by means of a digital filter; secondly, a compressed sensing theory is brought in to carry put compressed sampling on leakage acoustic emission signals; thirdly, the compressed signals are precisely reconstructed through an OMP algorithm; fourthly, EMD of the mask signal method is adopted to decompose the acoustic emission signals, and components of different frequency in the signals are sorted out from high frequency to low frequency one by one; fifthly, Hilbert transformation is conducted on the frequency components of the acoustic emission signals to determine starting time and ending time of the acoustic emission signals.

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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