基于VMD去噪及多尺度模糊熵的管道小泄漏研究

针对天然气管道微小泄漏信号的特征在单一尺度上难以全面提取的问题,提出一种基于变分模态分解(VMD)与多尺度模糊熵(MFE)结合的管道小泄漏信号识别方法.首先使用VMD算法对管道负压波信号进行降噪处理,通过欧氏距离(ED)法评估确定VMD分解的有效模态并对其进行重构,以重构信号信噪比最高原则确定VMD分解的模态个数;将多尺度模糊熵作为故障特征值向量,最后用支持向量机对特征值向量进行分类识别.实验结果表明:该方法对管道信号状态整体识别率达99.33％,证明了该方法总体识别效果较好,可实现对管道小泄漏信号的准确识别.

Research on Small Pipeline Leakage Based on VMD Denoising and Multi-scale Fuzzy Entropy

Aiming at the problem that it is difficult to fully extract the characteristics of small leakage signals of natural gas pipelines on a single scale, a method for identifying small leakage signals of pipelines based on the combination of variational modal decomposition (VMD) and multi-scale fuzzy entropy (MFE) is proposed. First, the VMD algorithm is used to denoise the pipeline negative pressure wave signal, and the effective mode of the VMD decomposition is determined and reconstructed by the Euclidean distance (ED) method to determine the VMD decomposition based on the principle of the highest signal-to-noise ratio of the reconstructed signal The number of modes; the multi-scale fuzzy entropy is used as the fault eigenvalue vector, and finally the support vector machine is used to classify and recognize the eigenvalue vector. The experimental results show that the overall recognition rate of the pipeline signal state by this method is 99.33%, which proves that the overall recognition effect of the method is good, and it can realize the accurate identification of small pipeline leakage signals.