Estimation of strain of distorted FBG sensor spectra using a fixed FBGfilter circuit and an artificial neural network

Kahandawa, GC; Epaarachchi, J; Lau, KT; Canning, J

Estimation of strain of distorted FBG sensor spectra using a fixed FBGfilter circuit and an artificial neural network

Kahandawa, GC Epaarachchi, J Lau, KT Canning, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013, 2013, 1 pp. 89 - 94
Issue Date:: 2013-08-09

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Field	Value	Language
dc.contributor.author	Kahandawa, GC	en_US
dc.contributor.author	Epaarachchi, J	en_US
dc.contributor.author	Lau, KT	en_US
dc.contributor.author	Canning, J https://orcid.org/0000-0001-8281-7783	en_US
dc.date.issued	2013-08-09	en_US
dc.identifier.citation	Proceedings of the 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013, 2013, 1 pp. 89 - 94	en_US
dc.identifier.isbn	9781467355001	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119923
dc.description.abstract	Fibre Bragg Grating (FBG) sensors are extremely sensitive to changes of strain, and are therefore an extremely useful candidate for Structural Health Monitoring (SHM) systems of composite structures. Sensitivity of FBGs to strain gradients originating from damage was observed as an indicator of initiation and propagation of damage in composite structures. To date there have been numerous research works done on distorted FBG spectra due to damage accumulation under controlled environments. Unfortunately, a number of related unresolved problems remain in FBG-based SHM systems development, making the present SHM systems unsuitable for real life applications. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in strain predictions. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2013 IEEE 8th International Conference on Intelligent Sensors, Sensor Networks and Information Processing: Sensing the Future, ISSNIP 2013	en_US
dc.relation.isbasedon	10.1109/ISSNIP.2013.6529770	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Estimation of strain of distorted FBG sensor spectra using a fixed FBGfilter circuit and an artificial neural network	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	en_US
utslib.for	0912 Materials Engineering	en_US
pubs.embargo.period	Not known	en_US
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	1	en_US

Abstract:

Fibre Bragg Grating (FBG) sensors are extremely sensitive to changes of strain, and are therefore an extremely useful candidate for Structural Health Monitoring (SHM) systems of composite structures. Sensitivity of FBGs to strain gradients originating from damage was observed as an indicator of initiation and propagation of damage in composite structures. To date there have been numerous research works done on distorted FBG spectra due to damage accumulation under controlled environments. Unfortunately, a number of related unresolved problems remain in FBG-based SHM systems development, making the present SHM systems unsuitable for real life applications. This paper reveals a novel configuration of FBG sensors to acquire strain reading and an integrated statistical approach to analyse data in real time. The proposed configuration has proven its capability to overcome practical constraints and the engineering challenges associated with FBG-based SHM systems. A fixed filter decoding system and an integrated artificial neural network algorithm for extracting strain from embedded FBG sensor were proposed and experimentally proved. Furthermore, the laboratory level experimental data was used to verify the accuracy of the system and it was found that the error levels were less than 0.3% in strain predictions. © 2013 IEEE.

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http://hdl.handle.net/10453/119923