A multi-peak detection algorithm for Fiber Bragg Grating sensing systems

Xu, Ouyang; Liu, Jingen; Tong, Xiaofeng; Zhang, Cui; Deng, Chengwei; Mao, Yan; Yin, Renjie; Jin, Chunjiao; Fang, Dingjiang

doi:10.1016/j.yofte.2020.102311

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…Upon reaching the minimum value for sum of squared errors, the λ B can be calculated. The X vector can be determined with LMA (Xu et al, 2020;Wang et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

Sadik

2023

Preprint

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This study introduces a novel approach to address the challenge of separating partially and fully overlapped spectra in Fiber Bragg Grating (FBG) sensors. Leveraging the Nonlinear Least-Squares Curve Fitting method with the Levenberg-Marquardt algorithm (LMA), the proposed algorithm successfully detects distinct FBG peaks even in complex spectral superpositions. This investigation utilized two Fiber Bragg Grating (FBG) sensors, with one subjected to varying temperatures and the other maintained at a constant temperature as a reference. The reflection spectra from the FBG sensors were analyzed across 20 distinct temperature values, and the proposed algorithm accurately identified the peak wavelengths of both sensors with precision. Examination of the relative errors in the detected peak wavelengths revealed a maximum percentage error of 0.0025%. This finding underscores the capability of the proposed algorithm to achieve highly accurate peak detection in densely multiplexed FBG sensors, particularly in applications demanding precise measurements across numerous points within a sensor network.

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“…Upon reaching the minimum value for sum of squared errors, the λ B can be calculated. The X vector can be determined with LMA (Xu et al, 2020;Wang et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

Sadik

2023

Preprint

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“…2(c), the lower limit is the best choice for the threshold value after the first-order derivation of the Hilbert transform in order to meet the needs of spectral decomposition and high-precision demodulation. That is, it can ensure the preservation of more data points after the decomposition of the reflection spectrum and improve the accuracy of multi-peak detection [11].…”

Section: Principle Of Spectral Segmentation Based On Hilbert Transformmentioning

confidence: 99%

A multi-peak demodulation algorithm based on Ht-Exg fiber grating sensing network

Shao¹,

Ye²,

Hong³

et al. 2023

International Conference on Optoelectronic Information and Functional Materials (OIFM 2023)

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Aiming at the poor accuracy of traditional single peak detection algorithm in fiber grating demodulation, this paper proposes a symmetry corrected Gaussian nonlinear fitting algorithm based on Hilbert transform (HT-EXG) for multi peak detection. The algorithm uses Hilbert transform to segment the input reflection spectrum and uses exponential modified Gaussian nonlinear fitting algorithm to correct and optimize its peak position, so as to achieve accurate positioning of the reflection spectrum. The experimental results show that the proposed HT-EXG algorithm improves the accuracy of FBG peak demodulation and the stability of the system. The average error of peak detection is less than 3 pm, and the system stability is better than 0.5 pm. It provides an accurate demodulation method for FBG sensor network peak detection.

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“…Due to the small refractive index change, the SPR signature almost remained during this experiment. Driven by the wish to calibrate this sensor probe automatically, first of all, the peak detection was conducted by comparing transmitted power of 11 continuous wavelength sampling points from 1539.5 nm, which looped to 1566 nm in turn [20][21][22][23]. The amount 11 of the wavelengths in one sequence is optimized to better filter out the spectral noise.…”

Section: Peak Identification and Cladding Mode Selectionmentioning

confidence: 99%

Cladding Mode Fitting-Assisted Automatic Refractive Index Demodulation Optical Fiber Sensor Probe Based on Tilted Fiber Bragg Grating and SPR

Lin

Huang

Liu

et al. 2022

Sensors

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In the paper based on surface plasmon resonance (SPR) in a tilted fiber Bragg grating (TFBG), a novel algorithm is proposed, which facilitates demodulation of surrounding refractive index (SRI) via cladding mode interrogation and accelerates calibration and measurement of SRI. Refractive indices with a tiny index step of 2.2 × 10−5 are prepared by the dilution of glucose aqueous solution for the test and the calibration of this fiber sensor probe. To accelerate the calibration process, automatic selection of the most sensitive cladding mode is demonstrated. First, peaks of transmitted spectrum are identified and numbered. Then, sensitivities of several potentially sensitive cladding modes in amplitude adjacent to the left of the SPR area are calculated and compared. After that, we focus on the amplitudes of the cladding modes as a function of a SRI, and the highest sensitivity of −6887 dB/RIU (refractive index unit) is obtained with a scanning time of 15.77 s in the range from 1520 nm to 1620 nm. To accelerate the scanning speed of the optical spectrum analyzer (OSA), the wavelength resolution is reduced from 0.028 nm to 0.07 nm, 0.14 nm, and 0.28 nm, and consequently the scanning time is shortened to 6.31 s, 3.15 s, and 1.58 s, respectively. However, compared to 0.028 nm, the SRI sensitivity for 0.07 nm, 0.14 nm, and 0.28 nm is reduced to −5685 dB/RIU (17.5% less), −5415 dB/RIU (21.4% less), and −4359 dB/RIU (36.7% less), respectively. Thanks to the calculation of parabolic equation and weighted Gauss fitting based on the original data, the sensitivity is improved to −6332 dB/RIU and −6721 dB/RIU, respectively, for 0.07 nm, and the sensitivity is increased to −5850 dB/RIU and −6228 dB/RIU, respectively, for 0.14 nm.

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A multi-peak detection algorithm for Fiber Bragg Grating sensing systems

Cited by 16 publications

References 11 publications

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

Nonlinear least-squares curve fitting method for detection of overlapped FBG temperature sensor spectra

A multi-peak demodulation algorithm based on Ht-Exg fiber grating sensing network

Cladding Mode Fitting-Assisted Automatic Refractive Index Demodulation Optical Fiber Sensor Probe Based on Tilted Fiber Bragg Grating and SPR

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