Natural gas pipeline small leakage feature extraction and recognition based on LMD envelope spectrum entropy and SVM |
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| Affiliation: | 1. School of Information Science and Engineering, Yanshan University, 438, Hebei Avenue, QinHuangDao, Hebei Province 066004, PR China;2. Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao 066004, Hebei Province, PR China;3. China Petroleum and Gas Pipeline Telecommunication and Electricity Engineering Corporation, Langfang 065000, Hebei Province, PR China;1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;2. School of Engineering and Digital Arts, University of Kent, Canterbury, Kent CT2 7NT, UK;3. Department of Mechanical Engineering, University of Sheffield, Sheffield S10 2TN, UK;1. Tongji University, State Key Laboratory of Disaster Reduction in Civil Engineering, Siping 1239, Shanghai 200092, China;2. Tongji University, College of Civil Engineering, Siping 1239, Shanghai 200092, China;1. College of Mechanical and Electronic Engineering, China University of Petroleum, Qingdao 266580, PR China;2. College of Engineering, Ocean University of China, Qingdao 266100, PR China;3. Department of Mechanical Engineering, University of Houston, Houston, TX 77004, USA;1. State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China;2. Binhai International Advanced Structural Integrity Research Centre, Tianjin 300072, China |
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| Abstract: | 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. |
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| Keywords: | Leak aperture recognition Local mean decomposition Principal PF components selection Envelope spectrum entropy SVM |
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