Publication: Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
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Date
2022
Authors
Nordin N.D.
Abdullah F.
Zan M.S.D.
Bakar A.A.A.
Krivosheev A.I.
Barkov F.L.
Konstantinov Y.A.
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg�Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subse-quent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4? C (428 kHz or 9.3 �?); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5? C (1.6 MHz or 35 �?). In this case, double processing is more effective for all SNRs. The described technique�s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Description
Brillouin scattering; Concretes; Curve fitting; Data handling; Extraction; Fiber optic sensors; Fiber optics; Learning algorithms; Machine learning; Structural health monitoring; BOTDA; Brillouin frequency shift extraction; Brillouin frequency shifts; Brillouin gain spectrum; Correlation techniques; Distributed fiber-optic sensors; Frequency shift; Generalized linear model; Low signal-to-noise ratio; Prediction accuracy; Signal to noise ratio; algorithm; fiber optics; noise; signal noise ratio; Algorithms; Fiber Optic Technology; Noise; Signal-To-Noise Ratio