关键词: 局域均值分解, 小波分析, 最小二乘双支持向量机, 泄漏孔径, 泄漏定位

Abstract: In fluid pipeline leak detection and location, noise in the pressure signal collected both ends of the pipeline will affects the accuracy of leak detection and the error of leakage location. To reduce the noise interference an improved local mean decomposition (LMD) signal analysis method is proposed. The production functions (PF) that were related to the leak signal can be exacted, and it was necessary to know the characteristics of leak signals or noise in advance. According to the cross⁃correlation function, there is a significant peak between the measured signals which are decomposed into a number of PFs. These reconstructed principles PF components were obtained, and a wavelet analysis was used to remove the noise in the reconstructed signal. On this basis, the signal features were extracted according to the time⁃domain feature and waveform feature, which were input into Least Squares Twin Support Vector Machine (LSTSVM), LSTSVM to distinguished different working conditions. According to the reconstructed signal after wavelet de⁃noising, the time delay estimate of the negative pressure signal at the end of the pipeline is obtained by the cross⁃correlation function, and the leak location was ultimately calculated by combining the time delay with the leak signal propagation velocity. A leak simulation for pipeline was proposed , where the collected data of the different working conditions was processed. The experimental results show that the proposed method can effectively identify different working conditions and accurately locate the leakage point.

Key words: Local mean decomposition, Wavelet analysis, Least squares twin support vector machine, Leak aperture, Leak localization