中国机械工程2017，Vol.28Issue(10)：1202-1209,8.DOI:10.3969/j.issn.1004-132X.2017.10.011
基于总体局域均值分解及稀疏表示分类的天然气管道泄漏孔径识别
Natural Gas Pipeline Leakage Aperture Identification Based on ELMD and SRC
摘要
Abstract
Natural gas pipeline leakage was influenced by the aperture,the sensor distance,the pressures in the pipeline and many factors,so the feature extraction and recognition algorithm is rela-tively complicated.A novel leak aperture identification method which combined feature extraction based on ELMD-KL model with SRC was proposed.ELMD was applied to adaptively decompose leak signals,to obtain characteristic informations of different aperture leak signals,and to extract the prin-cipal product function(PF)components based on KL divergence which contained the main leakage in-formations.The method extracted multiple characteristic parameters in time domain and frequency do-main as the feature vectors.For the classification of small sample complex signals,a SRC was put for-ward to realize the accurate classification of leak apertures.The classifier obtained the most sparse so-lutions of the test signals with overcomplete dictionary.The solutions were used as the sparse coeffi-cient to reconstruct the test signals and obtain reconstruction signals in different classes of the test signals.Finally,classification of leak apertures was accomplished by judging the residual values be-tween test signals and reconstruction signals.The experimental results show that the proposed algo-rithm has higher recognition accuracy compared with the traditional classification algorithm of SVM and BP.关键词
泄漏孔径识别/总体局域均值分解(ELMD)/KL散度/稀疏表示分类器/过完备字典Key words
leakage aperture identification/ensemble local mean decomposition (ELMD)/KL di-vergence/sparse representation classifier(SRC)/overcomplete dictionary分类
机械制造引用本文复制引用
孙洁娣,彭志涛,温江涛,王飞..基于总体局域均值分解及稀疏表示分类的天然气管道泄漏孔径识别[J].中国机械工程,2017,28(10):1202-1209,8.基金项目
国家自然科学基金资助项目(51204145) （51204145）
河北省自然科学基金资助项目(E2013203300,E2016203223) （E2013203300,E2016203223）
