High sensitivity of a distributed optical-fiber vibration sensing （DOVS） system based on the phase-sensitivity optical time domain reflectometry （Ф-OTDR） technology also brings in high nuisance alarm rates （NARs...High sensitivity of a distributed optical-fiber vibration sensing （DOVS） system based on the phase-sensitivity optical time domain reflectometry （Ф-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.展开更多
基金The authors gratefully acknowledge the supports provided for this research by Youth Foundation （Grant No. 61301275）, Major Instrument Special Program （Grant No. 41527805）, the Major Program （Grant No. 61290312） of the National Science Foundation of China （NSFC）, and the fund of State Grid Corporation of China： Research on distributed multi-parameter sensing and measurement control technology for electric power optical fiber communication networks （Grant No. 5455HT160014）. This work is also supported by Program for Changjiang Scholars and Innovative Research Team in University （PCSIRT, IRT1218） and the 111 Project （B14039）.
文摘High sensitivity of a distributed optical-fiber vibration sensing （DOVS） system based on the phase-sensitivity optical time domain reflectometry （Ф-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.
