Acta Optica Sinica, Volume. 39, Issue 10, 1006002(2019)

Wavelength Detection Optimization of Fiber Bragg Grating Sensing Networks Based on Distortion Spectrum

Hao Jiang1,2,3, Qingxu Zhou1,2, Jing Chen1,2、*, and Xiren Miao1,2
Author Affiliations
  • 1College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350108, China
  • 2Research Institute of Power System & Power Equipment, Fuzhou University, Fuzhou, Fujian 350108, China;
  • 3Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, Fujian 350108, China;
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    To address the problem of difficulty in distortion specttum demodulation of fiber Bragg grating (FBG) based sensing networks,we propose a wavelength demodulation technique based on estimation using a distribution algorithm (EDA). We construct a theoretical function of distortion spectrum based on the super Gaussian function and transform the wavelength detection problem of the distorted FBG sensing network into a function optimization problem. The proposed method is used to demodulate the distortion spectrum of a FBG sensing network through an experiment. The results denote that EDA can not only maintain an average detection accuracy within 1 pm even when the spectrum of FBG is distorted but also quantitatively estimate the distortion degree of FBG. When compared with the traditional peak detection methods, the proposed method can effectively identify the Bragg wavelength from a distortion spectrum. The proposed method provides a novel method to extend the service life and enhance the reliability of an FBG sensor network.

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    Hao Jiang, Qingxu Zhou, Jing Chen, Xiren Miao. Wavelength Detection Optimization of Fiber Bragg Grating Sensing Networks Based on Distortion Spectrum[J]. Acta Optica Sinica, 2019, 39(10): 1006002

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    Paper Information

    Category: Fiber Optics and Optical Communications

    Received: Jan. 21, 2019

    Accepted: Jun. 3, 2019

    Published Online: Oct. 9, 2019

    The Author Email: Chen Jing (chenj@fzu.edu.cn)

    DOI:10.3788/AOS201939.1006002

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