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Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring

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;...

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Main Authors: Nordin N.D., Abdullah F., Zan M.S.D., Bakar A.A.A., Krivosheev A.I., Barkov F.L., Konstantinov Y.A.
Other Authors: 57217851042
Format: Article
Published: MDPI 2023
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spelling my.uniten.dspace-269282023-05-29T17:37:52Z Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring Nordin N.D. Abdullah F. Zan M.S.D. Bakar A.A.A. Krivosheev A.I. Barkov F.L. Konstantinov Y.A. 57217851042 56613644500 24767242400 56926940300 57209360853 6603447196 55785515700 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 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. Final 2023-05-29T09:37:52Z 2023-05-29T09:37:52Z 2022 Article 10.3390/s22072677 2-s2.0-85127101908 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127101908&doi=10.3390%2fs22072677&partnerID=40&md5=ca112093cc9c21ea58c1fe25cd4e47ff https://irepository.uniten.edu.my/handle/123456789/26928 22 7 2677 All Open Access, Gold, Green MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
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
author2 57217851042
author_facet 57217851042
Nordin N.D.
Abdullah F.
Zan M.S.D.
Bakar A.A.A.
Krivosheev A.I.
Barkov F.L.
Konstantinov Y.A.
format Article
author Nordin N.D.
Abdullah F.
Zan M.S.D.
Bakar A.A.A.
Krivosheev A.I.
Barkov F.L.
Konstantinov Y.A.
spellingShingle Nordin N.D.
Abdullah F.
Zan M.S.D.
Bakar A.A.A.
Krivosheev A.I.
Barkov F.L.
Konstantinov Y.A.
Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
author_sort Nordin N.D.
title Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
title_short Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
title_full Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
title_fullStr Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
title_full_unstemmed Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring
title_sort improving prediction accuracy and extraction precision of frequency shift from low-snr brillouin gain spectra in distributed structural health monitoring
publisher MDPI
publishDate 2023
_version_ 1806426178826797056
score 13.214268

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