Unsupervised Representation Learning-Based Spectrum Reconstruction for Demodulation of Fabry-Perot Interferometer Sensor

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

Unsupervised Representation Learning-Based Spectrum Reconstruction for Demodulation of Fabry-Perot Interferometer Sensor

Ren, S Chen, S

Xu, H Hou, X Yang, Q 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, (12), pp. 13810-13816
Issue Date:: 2023-06-15

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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	Xu, H
dc.contributor.author	Hou, X
dc.contributor.author	Yang, Q
dc.contributor.author	Wang, G
dc.contributor.author	Shen, C
dc.date.accessioned	2024-03-26T04:22:32Z
dc.date.available	2024-03-26T04:22:32Z
dc.date.issued	2023-06-15
dc.identifier.citation	IEEE Sensors Journal, 2023, 23, (12), pp. 13810-13816
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/177172
dc.description.abstract	This article presents a novel unsupervised representation learning-based demodulation framework for Fabry-Perot interferometer (FPI) sensors, which is a straightforward and effective solution for obtaining an interferometric spectrum without any optical spectrum analyzers (OSAs). The proposed framework utilizes a simple spectrum reconstruction method to reconstruct the FPI sensor's spectrum using relatively low-scale sample points, requiring less manual effort than conventional approaches. The proposed approach involves two steps: first, an optical system converts the FPI sensing signal to transmitted intensity, and second, the unsupervised representation learning-based reconstruction framework establishes a nonlinear relationship between the intensity signal and the actual changing spectrum. The proposed approach is validated using real-world datasets generated from pressure performance tests, achieving excellent performance with a reconstruction error of 0.039 nm and a range of 73 nm. The results demonstrate the practical potential viability of the proposed framework for large-scale remote monitoring systems.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2023.3272954
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	Unsupervised Representation Learning-Based Spectrum Reconstruction for Demodulation of Fabry-Perot Interferometer Sensor
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	2024-03-26T04:22:31Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	12

Abstract:

This article presents a novel unsupervised representation learning-based demodulation framework for Fabry-Perot interferometer (FPI) sensors, which is a straightforward and effective solution for obtaining an interferometric spectrum without any optical spectrum analyzers (OSAs). The proposed framework utilizes a simple spectrum reconstruction method to reconstruct the FPI sensor's spectrum using relatively low-scale sample points, requiring less manual effort than conventional approaches. The proposed approach involves two steps: first, an optical system converts the FPI sensing signal to transmitted intensity, and second, the unsupervised representation learning-based reconstruction framework establishes a nonlinear relationship between the intensity signal and the actual changing spectrum. The proposed approach is validated using real-world datasets generated from pressure performance tests, achieving excellent performance with a reconstruction error of 0.039 nm and a range of 73 nm. The results demonstrate the practical potential viability of the proposed framework for large-scale remote monitoring systems.

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http://hdl.handle.net/10453/177172