Study on recognition method based on RBF neural network for <br /> intrusion events of oil and gas pipeline<br />
Journal Title: Science Paper Online - Year 2006, Vol 1, Issue 3
Abstract
A recognition method for intrusion events is studied, which is used in the distributed optical fiber pre-warning system for the safety of oil and gas pipeline. In this pre-warning system, which is based on the principle of Mach-Zehnder optical fiber interferometer, an optical cable is laid along the pipeline in the same ditch and three single mode optical fibers in the optical cable build up the distributed micro-vibrant measuring sensor. The system can judge whether intrusion events have occurred by detecting the vibration signals along the pipeline in real-time, extracting the eigenvectors of the vibration signals by the “energy-status” method based on wavelet package analysis, and the recognition through the RBF neural network. Subsequently the position of the intrusion events can be located. Finally, the data obtained at oil field prove the effectiveness of this method.
Authors and Affiliations
Shijiu Jin , Zhigang Qu, Yan Zhou
Keywords
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- EP ID EP154872
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