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.
Highlights
The distributed optical-fiber vibration sensing (DOVS) system based on the phase-sensitivity optical time domain reflectometry (Φ-OTDR) technology provides a convenient and cost-effective disturbance detection and location method for safety monitoring of ultra-long distance perimeters, oil or gas pipelines, submarine or buried telecommunication cables, power transmission cables, and large structures [1]
The high sensitivity of the system induces that it is liable to be interfered by complicated disturbing sources in practical long-distance monitoring applications, which may result in a high nuisance alarm rate (NAR) [5]
DOVS signals are comparatively studied in this paper to improve the perturbing event identification performance: energy spectrum distributions are extracted by using the wavelet decomposition (WD) and the wavelet packet decomposition (WPD) respectively for the varied types of disturbing event signals along the buried oil pipeline of about 65 km in practical field applications
Summary
The distributed optical-fiber vibration sensing (DOVS) system based on the phase-sensitivity optical time domain reflectometry (Φ-OTDR) technology provides a convenient and cost-effective disturbance detection and location method for safety monitoring of ultra-long distance perimeters, oil or gas pipelines, submarine or buried telecommunication cables, power transmission cables, and large structures [1]. Most of the related signal processing work focused on the improvement of detection, and very few mentioned the identification of the perturbing events except some frequency spectrum analysis with the fast Fourier transform (FFT) [10]. DOVS signals are comparatively studied in this paper to improve the perturbing event identification performance: energy spectrum distributions are extracted by using the wavelet decomposition (WD) and the wavelet packet decomposition (WPD) respectively for the varied types of disturbing event signals along the buried oil pipeline of about 65 km in practical field applications. The practical application results show that the probability for correct detection has been improved considerably and NAR has been reduced effectively
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