关键词: 集合经验模态分解, 相位随机化技术, 多尺度, 管道泄漏检测

Abstract: The EEMD(Ensemble Empirical Mode Decomposition) algorithm faces challenges in aligning the generated IMF(Intrinsic Mode Function) components during the decomposition process. To address this issue, a novel denoising method that combines EEMD with the PRT(Phase Randomization Technique) is proposed, enhancing the denoising performance of the improved EEMD algorithm. By incorporating PRT, the method effectively handles nonlinear and nonstationary signals, significantly improving the stability and reliability of the IMFs, and enhances the performance of the EEMD algorithm in noisy environments. The experimental results strongly demonstrate the innovation’s value, as the EEMD-PRT algorithm shows superior performance compared to traditional methods by improving the signal-to-noise ratio and correlation coefficient of noisy signals, reducing the mean square error and mean absolute error. Furthermore, its effectiveness has been thoroughly validated in pipeline leak detection for pipes with varying diameters.

Key words: ensemble empirical mode decomposition, phase randomization technique , multi-scale, pipeline leak detection