• CN:11-2187/TH
  • ISSN:0577-6686

›› 2014, Vol. 50 ›› Issue (20): 18-25.doi: 10.3901/JME.2014.20.018

• 论文 • 上一篇    下一篇



  1. 燕山大学信息科学与工程学院 燕山大学河北省测试计量技术及仪器重点实验室 中国石油天然气管道通信电力工程总公司
  • 出版日期:2014-10-20 发布日期:2014-10-20

Gas Pipeline Small Leak Aperture Classification Based on Local Mean Decomposition Envelope Spectrum Entropy and SVM

SUN Jiedi; XIAO Qiyang; WEN Jiangtao; WANG Fei   

  • Online:2014-10-20 Published:2014-10-20

摘要: 针对管道泄漏信号的非平稳特征以及管道泄漏孔径大小难以识别的问题,提出一种基于局域均值分解包络谱熵及支持向量机的识别方法。该方法对管道泄漏信号进行局域均值分解,得到若干个瞬时频率具有物理意义的乘积函数(production Function, PF)分量;计算各PF分量的峭度值并据此选出包含主要泄漏信息的分量作为主PF分量,对这些分量进一步采用小波包分解能量法进行分析并重构;再对重构后的主PF分量进行希尔伯特变换求取包络谱,结合信息熵的概念提出包络谱熵并计算熵值;将归一化包络谱熵作为泄漏信号特征输入支持向量机分类器中,用以区分不同的泄漏孔径,完成对泄漏孔径的识别。通过试验采集大量的管道泄漏信号进行处理及分析,试验结果表明该方法能有效识别不同泄漏孔径类别。

关键词: 包络谱熵, 管道微小泄漏识别, 局域均值分解, 支持向量机

Abstract: When small leak occurs in the natural gas pipeline, it is difficult to identify the leak scale and aperture. It is proposed a small leak aperture recognition method based on local mean decomposition(LMD) envelope spectrum entropy and SVM. The leakage signals are decomposed into a number of production functions(PFs) components which have physical significance instantaneous frequencies. And then calculate the PFs kurtosis values and according to this select the principal PF components which contain most of leakage information. Further the wavelet packet decomposition and band energy distribution method are used to analyze the principal PF components and then reconstruct them. The Hilbert transform is applied to these reconstructed principal PF components and the corresponding envelope spectrums are obtained. Combining the concept of information entropy, the envelope spectrum entropy is proposed and calculates the entropy values. The normalized envelope spectrum entropy as the leakage feature is input the support vector machine(SVM) and the leak aperture classification is accomplished. By analyzing the acquired pipeline leakage signals in the field experiments, the results show that this method can effectively identify the different leak apertures.

Key words: envelope spectrum entropy, local mean decomposition, pipeline small leak recognition, support vector machine(SVM)