基于改进 ELMD 和多尺度熵的管道泄漏信号识别

This paper is conducted with the aim of preventing urban pipeline leakage accidents and accurately extracting the characteristics of pipeline leakage signals. Firstly, an improved ELMD and multiscale entropy pipeline leakage signal identification method was proposed. The signal at the end point was processed by using the peak waveform matching extension method so as to attenuate the distortion of signal components. Secondly, ELMD decomposition of the original leakage signal was carried out to obtain a series of product functions values, the value of whose components were calculated through multi-scale entropy. The PF component containing the main leakage information was screened according to entropy value to eliminate the impact of background noise. Finally, a BP neural network was constructed to identify leakage signals. The results show that the proposed method, reducing errors after decomposition, is able to detect pipeline leakage, and it works better in recognizing leakage signals compared with unmodified ELMD method.