Dual-Parameter Demodulation of FBG-FPI Cascade Sensors via Sparse Samples: A Deep Learning-Based Perspective

Xu, H; Chen, S; Ren, S; Hou, X; Wang, G; Shen, C

Dual-Parameter Demodulation of FBG-FPI Cascade Sensors via Sparse Samples: A Deep Learning-Based Perspective

Xu, H Chen, S

Ren, S Hou, X Wang, G Shen, C

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2023, 23, (19), pp. 23903-23915
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Xu, H
dc.contributor.author	Chen, S https://orcid.org/0000-0001-9992-2264
dc.contributor.author	Ren, S
dc.contributor.author	Hou, X
dc.contributor.author	Wang, G
dc.contributor.author	Shen, C
dc.date.accessioned	2024-03-26T04:27:15Z
dc.date.available	2024-03-26T04:27:15Z
dc.date.issued	2023-10-01
dc.identifier.citation	IEEE Sensors Journal, 2023, 23, (19), pp. 23903-23915
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/177177
dc.description.abstract	This article proposes a complete framework for the manufacturing and dual-parameter demodulation of sensor systems consisting of cascaded fiber Bragg gratings (FBGs) and Fabry-Perot interferometers (FPIs). The proposed system aims to enable simultaneous interrogation of the external strain and temperature using a machine learning (ML) technique, with limited data availability and without requiring an optical spectrum analyzer (OSA). The system converts the sensing signals of a cascaded FBG-FPI sensor into changes in the transmitted optical intensity across multiple channels of the array waveguide grating (AWG). By inputting the transmitted optical intensity and wavelength shift data into a neural network, an intricate nonlinear relationship is established, which enables precise interrogation of the peak wavelength. Furthermore, to overcome the common limitation of insufficient data in data-driven models, we introduce a high-performance data augmentation method that utilizes a generative adversarial network (GAN) to rapidly augment the dataset during the training process. Extensive experiments using a real-world dataset generated in an industrial optical fiber sensing scenario demonstrate the effectiveness and superiority of the proposed framework.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2023.3308172
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	40 Engineering
dc.title	Dual-Parameter Demodulation of FBG-FPI Cascade Sensors via Sparse Samples: A Deep Learning-Based Perspective
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
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	2024-03-26T04:27:14Z
pubs.issue	19
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	19

Abstract:

This article proposes a complete framework for the manufacturing and dual-parameter demodulation of sensor systems consisting of cascaded fiber Bragg gratings (FBGs) and Fabry-Perot interferometers (FPIs). The proposed system aims to enable simultaneous interrogation of the external strain and temperature using a machine learning (ML) technique, with limited data availability and without requiring an optical spectrum analyzer (OSA). The system converts the sensing signals of a cascaded FBG-FPI sensor into changes in the transmitted optical intensity across multiple channels of the array waveguide grating (AWG). By inputting the transmitted optical intensity and wavelength shift data into a neural network, an intricate nonlinear relationship is established, which enables precise interrogation of the peak wavelength. Furthermore, to overcome the common limitation of insufficient data in data-driven models, we introduce a high-performance data augmentation method that utilizes a generative adversarial network (GAN) to rapidly augment the dataset during the training process. Extensive experiments using a real-world dataset generated in an industrial optical fiber sensing scenario demonstrate the effectiveness and superiority of the proposed framework.

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http://hdl.handle.net/10453/177177