Literature DB >> 31817736

Eliminating Phase Drift for Distributed Optical Fiber Acoustic Sensing System with Empirical Mode Decomposition.

Yuejuan Lv1, Pengfei Wang2, Yu Wang1, Xin Liu1, Qing Bai1, Peihong Li1, Hongjuan Zhang2, Yan Gao2, Baoquan Jin1,3.   

Abstract

Phase-drift elimination is crucial to vibration recovery in the coherent detection phase-sensitive optical time domain reflectometry system. The phase drift drives the whole phase signal fluctuation as a baseline, and its negative effect is obvious when the detection time is long. In this paper, empirical mode decomposition (EMD) is presented to extract and eliminate the phase drift adaptively. It decomposes the signal by utilizing the characteristic time scale of the data, and the baseline is eventually obtained. It is validated by theory and experiment that the phase drift deteriorates seriously when the length of the vibration region increases. In an experiment, the phase drift was eliminated under the conditions of different vibration frequencies of 1 Hz, 5 Hz, and 10 Hz. The phase drift was also eliminated with different vibration intensities. Furthermore, the linear relationship between phase and vibration intensity is demonstrated with a correlation coefficient of 99.99%. The vibrations at 0.5 Hz and 0.3 Hz were detected with signal-to-noise ratios (SNRs) of 55.58 dB and 64.44 dB. With this method, when the vibration frequency is at the level of Hz or sub-Hz, the phase drift can be eliminated. This contributes to the detection and recovery of low-frequency perturbation events in practical applications.

Entities:  

Keywords:  distributed acoustic sensing system; empirical mode decomposition; phase drift elimination; phase recovery; phase-sensitive optical time domain reflectometry

Year:  2019        PMID: 31817736     DOI: 10.3390/s19245392

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  High-Accuracy Event Classification of Distributed Optical Fiber Vibration Sensing Based on Time-Space Analysis.

Authors:  Zhao Ge; Hao Wu; Can Zhao; Ming Tang
Journal:  Sensors (Basel)       Date:  2022-03-07       Impact factor: 3.576

  1 in total

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