Feature Extraction and Identification in Distributed Optical-Fiber Vibration Sensing System for Oil Pipeline Safety Monitoring

Huijuan WU; Ya QIAN; Wei ZHANG; Chenghao TANG

doi:doi:10.1007/s13320-017-0360-1

Photonic Sensors, 2017, 7 (4): 305, Published Online: Jan. 9, 2018

Feature Extraction and Identification in Distributed Optical-Fiber Vibration Sensing System for Oil Pipeline Safety Monitoring

论文大纲

Huijuan WU ^*Ya QIAN Wei ZHANG Chenghao TANG

Author Affiliations

Key Laboratory of Optical Fiber Sensing and Communications, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 611731, China

Distributed optical-fiber vibration sensing Φ-OTDR pattern recognition multi-scale analysis

Abstract

High sensitivity of a distributed optical-fiber vibration sensing (DOVS) system based on the phase-sensitivity optical time domain reflectometry (Φ-OTDR) technology also brings in high nuisance alarm rates (NARs) in real applications. In this paper, feature extraction methods of wavelet decomposition (WD) and wavelet packet decomposition (WPD) are comparatively studied for three typical field testing signals, and an artificial neural network (ANN) is built for the event identification. The comparison results prove that the WPD performs a little better than the WD for the DOVS signal analysis and identification in oil pipeline safety monitoring. The identification rate can be improved up to 94.4%, and the nuisance alarm rate can be effectively controlled as low as 5.6% for the identification network with the wavelet packet energy distribution features.

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Huijuan WU, Ya QIAN, Wei ZHANG, Chenghao TANG. Feature Extraction and Identification in Distributed Optical-Fiber Vibration Sensing System for Oil Pipeline Safety Monitoring[J]. Photonic Sensors, 2017, 7(4): 305.

Feature Extraction and Identification in Distributed Optical-Fiber Vibration Sensing System for Oil Pipeline Safety Monitoring

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