کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7125128 | 1461532 | 2014 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM](/preview/png/7125128.png)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 55, September 2014, Pages 434-443
Journal: Measurement - Volume 55, September 2014, Pages 434-443
نویسندگان
Jiedi Sun, Qiyang Xiao, Jiangtao Wen, Fei Wang,