Feature extraction based on variational mode decomposition and support vector machine for natural gas pipeline leakage

被引：15

作者：

Lu, Jingyi ^{[1
,2
]}

Yue, Jikang ^{[1
,2
]}

Jiang, Chunlei ^{[1
]}

Liang, Hongwei ^{[1
]}

Zhu, Lijuan ^{[1
]}

机构：

[1] Northeast Petr Univ, Coll Elect & Informat Engn, Daqing 163318, Heillongjiang, Peoples R China

[2] Northeast Petr Univ, Heilongjiang Prov Key Lab Networking & Intelligen, Daqing, Heillongjiang, Peoples R China

来源：

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL | 2020年 / 42卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Variational mode decomposition; cloud model characteristic entropy; root mean square; correlation coefficient; support vector machine; LOCATION; SIGNAL; DIAGNOSIS; PIPES; LMD;

D O I：

10.1177/0142331219874161

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Issues concerning natural gas pipeline leakage are becoming more prominent than ever because of the continuing expansion of natural gas pipeline networks. Although many scholars have extensively investigated generation and detection methods for pipeline leakage acoustic signals, systematic research on the characteristics of leakage and interference signals remains insufficient. Results show that the method based on the RBF kernel function is feasible for pipeline fault diagnosis, yielding 100% sensitivity, 92% specificity, and 96% accuracy.

引用

页码：759 / 769

页数：11

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