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

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dc.citedby1
dc.contributor.authorNordin N.D.en_US
dc.contributor.authorAbdullah F.en_US
dc.contributor.authorZan M.S.D.en_US
dc.contributor.authorBakar A.A.A.en_US
dc.contributor.authorKrivosheev A.I.en_US
dc.contributor.authorBarkov F.L.en_US
dc.contributor.authorKonstantinov Y.A.en_US
dc.contributor.authorid57217851042en_US
dc.contributor.authorid56613644500en_US
dc.contributor.authorid24767242400en_US
dc.contributor.authorid56926940300en_US
dc.contributor.authorid57209360853en_US
dc.contributor.authorid6603447196en_US
dc.contributor.authorid55785515700en_US
dc.date.accessioned2023-05-29T09:37:52Z
dc.date.available2023-05-29T09:37:52Z
dc.date.issued2022
dc.descriptionBrillouin 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 Ratioen_US
dc.description.abstractIn 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2677
dc.identifier.doi10.3390/s22072677
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85127101908
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127101908&doi=10.3390%2fs22072677&partnerID=40&md5=ca112093cc9c21ea58c1fe25cd4e47ff
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26928
dc.identifier.volume22
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSensors
dc.titleImproving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoringen_US
dc.typeArticleen_US
dspace.entity.typePublication
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