High-efficiency FBG array sensor interrogation system via a neural network working with sparse data.

Ren, S; Chen, S; Yang, J; Wang, J; Yang, Q; Xue, C; Wang, G; Huang, M

High-efficiency FBG array sensor interrogation system via a neural network working with sparse data.

Ren, S Chen, S

Yang, J Wang, J Yang, Q Xue, C Wang, G Huang, M

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Publisher:: Optica Publishing Group
Publication Type:: Journal Article
Citation:: Opt Express, 2023, 31, (5), pp. 8937-8952
Issue Date:: 2023-02-27

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Field	Value	Language
dc.contributor.author	Ren, S
dc.contributor.author	Chen, S https://orcid.org/0000-0001-9992-2264
dc.contributor.author	Yang, J
dc.contributor.author	Wang, J
dc.contributor.author	Yang, Q
dc.contributor.author	Xue, C
dc.contributor.author	Wang, G
dc.contributor.author	Huang, M
dc.date.accessioned	2024-03-26T04:34:21Z
dc.date.available	2024-03-26T04:34:21Z
dc.date.issued	2023-02-27
dc.identifier.citation	Opt Express, 2023, 31, (5), pp. 8937-8952
dc.identifier.issn	1094-4087
dc.identifier.issn	1094-4087
dc.identifier.uri	http://hdl.handle.net/10453/177181
dc.description.abstract	FBG array sensors have been widely used in the multi-point monitoring of large structures due to their excellent optical multiplexing capability. This paper proposes a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN). The stress variations applied to the FBG array sensor are encoded by the array waveguide grating (AWG) as transmitted intensities under different channels and fed to an end-to-end NN model, which receives them and simultaneously establishes a complex nonlinear relationship between the transmitted intensity and the actual wavelength to achieve absolute interrogation of the peak wavelength. In addition, a low-cost data augmentation strategy is introduced to break the data size bottleneck common in data-driven methods so that the NN can still achieve superior performance with small-scale data. In summary, the demodulation system provides an efficient and reliable solution for multi-point monitoring of large structures based on FBG array sensors.
dc.format	Print
dc.language	eng
dc.publisher	Optica Publishing Group
dc.relation.ispartof	Opt Express
dc.relation.isbasedon	10.1364/OE.479708
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Optics
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	5102 Atomic, molecular and optical physics
dc.title	High-efficiency FBG array sensor interrogation system via a neural network working with sparse data.
dc.type	Journal Article
utslib.citation.volume	31
utslib.location.activity	United States
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-26T04:34:20Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	31
utslib.citation.issue	5

Abstract:

FBG array sensors have been widely used in the multi-point monitoring of large structures due to their excellent optical multiplexing capability. This paper proposes a cost-effective demodulation system for FBG array sensors based on a Neural Network (NN). The stress variations applied to the FBG array sensor are encoded by the array waveguide grating (AWG) as transmitted intensities under different channels and fed to an end-to-end NN model, which receives them and simultaneously establishes a complex nonlinear relationship between the transmitted intensity and the actual wavelength to achieve absolute interrogation of the peak wavelength. In addition, a low-cost data augmentation strategy is introduced to break the data size bottleneck common in data-driven methods so that the NN can still achieve superior performance with small-scale data. In summary, the demodulation system provides an efficient and reliable solution for multi-point monitoring of large structures based on FBG array sensors.

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http://hdl.handle.net/10453/177181