DocumentCode :
3503319
Title :
Leakage detection of natural gas pipeline based on neural networks and data fusion
Author :
Bingkun Gao ; Guojun Shi ; Qing Wang
Author_Institution :
Sch. of Electr. Eng. & Inf., Northeast Pet. Univ., DaQing, China
Volume :
02
fYear :
2013
fDate :
16-18 Aug. 2013
Firstpage :
1171
Lastpage :
1175
Abstract :
It was important to detect the leaking of natural gas pipeline whether leakage happened, the comprehensive and comparative analysis of various methods of leak detection indicated that most common detection method which was difficult to identify and apply to the natural gas pipeline. In this paper the method was proposed based on RBF neural network and the data fusion of D-S evidence theory for detecting the pipeline leak. Extracted neural network´s input parameter through wavelet denoising, then put the parameter to neural network and calculated by multi-sensor data fusion algorithm so as to acquire leaking information.
Keywords :
natural gas technology; neural nets; pipelines; production engineering computing; sensor fusion; signal denoising; wavelet transforms; D-S evidence theory; RBF neural network; data fusion; leakage detection; multisensor data fusion algorithm; natural gas pipeline; neural networks; wavelet denoising; Biological neural networks; Data integration; Leak detection; Neurons; Pipelines; D-S evidence theory; RBF neural network; data fusion; leak detection; wavelet denoising;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Measurement, Information and Control (ICMIC), 2013 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4799-1390-9
Type :
conf
DOI :
10.1109/MIC.2013.6758167
Filename :
6758167
Link To Document :
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