期刊
MEASUREMENT
卷 55, 期 -, 页码 434-443出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2014.05.012
关键词
Leak aperture recognition; Local mean decomposition; Principal PF components selection; Envelope spectrum entropy; SVM
资金
- National Natural Science Foundation of China [51204145]
- Natural Science Foundation of Hebei Province of China [E2013203300]
As it is difficult to identify the scale and aperture of small leaks occurring in a natural gas pipeline, this paper proposes a small leak feature extraction and recognition method based on local mean decomposition (LMD) envelope spectrum entropy and support vector machine (SVM). First, LMD is used to decompose the leakage signals into several FM-AM signals, i.e. into product function (PF) components. Then, based on their kurtosis features, the principal PF components that contain most of the leakage information are selected. Wavelet packet decomposition and energy methods are used to analyze and then reconstruct the principal PF components. The Hilbert transform is applied to these reconstructed principal PF components in order to acquire the envelope spectrum, from which the envelope spectrum entropy is obtained. Finally the normalized envelope spectrum entropy features are input into the SVM as leakage feature vectors in order to enable leak aperture category identification. By analyzing the acquired pipeline leakage signals in field experiments, it shows that this method can effectively identify different leak categories. (C) 2014 Elsevier Ltd. All rights reserved.
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