Recognition and Localization of FBG Temperature Sensing Based on Combined CDAE and 1-DCNN

In quasi-distributed fiber Bragg grating (FBG) temperature sensor networks, noise and spectral distortions affect the demodulation accuracy of the fiber gratings. To address this issue, we construct a sensor network using spectral encoding and propose a novel approach that combines convolutional den...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE sensors journal Ročník 24; číslo 7; s. 10125 - 10137
Hlavní autori:	Jiang, Hong, Tang, Rui, Wang, Chenyang, Zhao, Yihan, Li, Hao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	1-D convolutional neural network (1-DCNN) Accuracy Artificial neural networks Bragg gratings convolutional denoising autoencoder (CDAE) Demodulation fiber Bragg grating (FBG) Fiber gratings Goodness of fit Location awareness Machine learning Noise reduction Optical fiber sensors Recognition Reflection Root-mean-square errors Sensors Spectra spectral distortions Temperature temperature sensor networks Temperature sensors
ISSN:	1530-437X, 1558-1748
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	In quasi-distributed fiber Bragg grating (FBG) temperature sensor networks, noise and spectral distortions affect the demodulation accuracy of the fiber gratings. To address this issue, we construct a sensor network using spectral encoding and propose a novel approach that combines convolutional denoising autoencoder (CDAE) and 1-D convolutional neural network (1-DCNN), where CDAE is used for denoising FBG reflection spectra and 1-DCNN is employed for temperature state recognition and temperature demodulation of FBG sensors. The proposed method applies to FBG reflection spectra with different input SNR levels ranging from 0 to 20 dB. Experimental results demonstrate that this CDAE is effective in high-fidelity denoising of the original spectral signals and it outperforms other machine learning techniques. The 1-DCNN model achieves a recognition accuracy of 98.2% for FBG temperature states, with a goodness-of-fit value of 0.9994 for the relationship curve between predicted and actual temperatures, and a root-mean-square error (RMSE) of only 0.3049 °C. This research provides an efficient solution for FBG-based sensor networks.
AbstractList	In quasi-distributed fiber Bragg grating (FBG) temperature sensor networks, noise and spectral distortions affect the demodulation accuracy of the fiber gratings. To address this issue, we construct a sensor network using spectral encoding and propose a novel approach that combines convolutional denoising autoencoder (CDAE) and 1-D convolutional neural network (1-DCNN), where CDAE is used for denoising FBG reflection spectra and 1-DCNN is employed for temperature state recognition and temperature demodulation of FBG sensors. The proposed method applies to FBG reflection spectra with different input SNR levels ranging from 0 to 20 dB. Experimental results demonstrate that this CDAE is effective in high-fidelity denoising of the original spectral signals and it outperforms other machine learning techniques. The 1-DCNN model achieves a recognition accuracy of 98.2% for FBG temperature states, with a goodness-of-fit value of 0.9994 for the relationship curve between predicted and actual temperatures, and a root-mean-square error (RMSE) of only 0.3049 °C. This research provides an efficient solution for FBG-based sensor networks.
Author	Jiang, Hong Wang, Chenyang Tang, Rui Zhao, Yihan Li, Hao
Author_xml	– sequence: 1 givenname: Hong orcidid: 0000-0002-1915-2931 surname: Jiang fullname: Jiang, Hong email: jianghong@ccut.edu.cn organization: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China – sequence: 2 givenname: Rui surname: Tang fullname: Tang, Rui email: tangrui0907@163.com organization: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China – sequence: 3 givenname: Chenyang surname: Wang fullname: Wang, Chenyang email: 2202104012@stu.ccut.edu.cn organization: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China – sequence: 4 givenname: Yihan surname: Zhao fullname: Zhao, Yihan email: 2202004021@stu.ccut.edu.cn organization: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China – sequence: 5 givenname: Hao surname: Li fullname: Li, Hao email: ccutlihao@163.com organization: School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
BookMark	eNp9kM1OwzAQhC1UJNrCAyBxiMQ5xY7tOD626Q-gqki0SHCynGRTuWrt4qQHeHqStgfEgdOOVt_MaqeHOtZZQOiW4AEhWD48LyeLQYQjNqA05lLyC9QlnCchESzptJrikFHxfoV6VbXBmEjBRRd9vELu1tbUxtlA2yKYu1xvzbc-LlwZTEezYAW7PXhdHzwES7CVsetgpCsogoZJ3S4zttHpeDg5RpBwnC4W1-iy1NsKbs6zj96mk1X6GM5fZk_pcB7mkWR1SEsgWZTEQuuswCxOmBRxRngpBfBM5CAx01FEtI6oZlAAMGBZVpaUxZATTPvo_pS79-7zAFWtNu7gbXNSUUwJlRJL3lDiROXeVZWHUuWmPj5Ze222imDV9qjaHlXbozr32DjJH-fem532X_967k4eAwC_eMYoZYL-AEUBfv8
CODEN	ISJEAZ
CitedBy_id	crossref_primary_10_1109_TIM_2025_3558789 crossref_primary_10_1061_JPSEA2_PSENG_1902 crossref_primary_10_1016_j_measurement_2024_115918 crossref_primary_10_3390_electronics13204127 crossref_primary_10_1109_JSEN_2025_3543132
Cites_doi	10.1016/j.yofte.2022.103136 10.1364/oe.447670 10.1007/s13320-021-0615-8 10.3390/s22134951 10.1109/50.618322 10.1117/12.700807 10.1117/12.2195016 10.1364/oe.489964 10.1364/oe.479689 10.1109/cvpr.2016.90 10.3390/s110403466 10.2478/cee-2023-0037 10.1109/jsen.2016.2516038 10.1117/1.oe.58.7.072007 10.1016/j.ifacol.2022.09.200 10.1364/ao.46.002205 10.1088/1742-6596/2293/1/012006 10.3390/s17102368 10.1016/s0030-3992(99)00071-7 10.1109/jlt.2020.2971240 10.1088/1361-6501/aa637f 10.1109/jlt.2016.2593260 10.1364/oe.413443 10.1109/tbme.2015.2468589 10.1016/s0030-4018(02)01907-7 10.3390/ma14051207 10.1109/jlt.2018.2879119 10.1109/jlt.2022.3155253 10.1109/jsen.2023.3238028
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
DBID	97E RIA RIE AAYXX CITATION 7SP 7U5 8FD L7M
DOI	10.1109/JSEN.2024.3365995
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Solid State and Superconductivity Abstracts Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Solid State and Superconductivity Abstracts
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Engineering
EISSN	1558-1748
EndPage	10137
ExternalDocumentID	10_1109_JSEN_2024_3365995 10443347
Genre	orig-research
GrantInformation_xml	– fundername: Jilin Province Development and Reform Commission grantid: 2022C045-1 funderid: 10.13039/100015800 – fundername: Jilin Scientific and Technological Development Program grantid: 20210101479JC funderid: 10.13039/501100013061 – fundername: Jilin Scientific and Technological Development Program grantid: 20220203051SF funderid: 10.13039/501100013061
GroupedDBID	-~X 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK AENEX AGQYO AHBIQ AJQPL AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 EBS F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS TWZ AAYXX CITATION 7SP 7U5 8FD L7M
ID	FETCH-LOGICAL-c294t-3fe1b2867aabd04684976b15f97e5b7ce904a221aa23a4edee4e4bbff346ec103
IEDL.DBID	RIE
ISICitedReferencesCount	9
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001245628200229&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1530-437X
IngestDate	Thu Sep 11 00:15:37 EDT 2025 Sat Nov 29 06:39:55 EST 2025 Tue Nov 18 21:39:59 EST 2025 Wed Aug 27 02:17:09 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	7
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c294t-3fe1b2867aabd04684976b15f97e5b7ce904a221aa23a4edee4e4bbff346ec103
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-1915-2931
PQID	3031399095
PQPubID	75733
PageCount	13
ParticipantIDs	crossref_citationtrail_10_1109_JSEN_2024_3365995 crossref_primary_10_1109_JSEN_2024_3365995 ieee_primary_10443347 proquest_journals_3031399095
PublicationCentury	2000
PublicationDate	2024-04-01
PublicationDateYYYYMMDD	2024-04-01
PublicationDate_xml	– month: 04 year: 2024 text: 2024-04-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE sensors journal
PublicationTitleAbbrev	JSEN
PublicationYear	2024
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref12 ref15 ref14 ref31 ref11 Kingma (ref30) 2014 ref10 ref2 ref17 ref16 ref19 ref18 Shang (ref1) 2020; 46 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5
References_xml	– ident: ref16 doi: 10.1016/j.yofte.2022.103136 – ident: ref17 doi: 10.1364/oe.447670 – ident: ref2 doi: 10.1007/s13320-021-0615-8 – ident: ref24 doi: 10.3390/s22134951 – ident: ref23 doi: 10.1109/50.618322 – ident: ref9 doi: 10.1117/12.700807 – ident: ref12 doi: 10.1117/12.2195016 – ident: ref18 doi: 10.1364/oe.489964 – ident: ref25 doi: 10.1364/oe.479689 – ident: ref27 doi: 10.1109/cvpr.2016.90 – ident: ref19 doi: 10.3390/s110403466 – ident: ref4 doi: 10.2478/cee-2023-0037 – ident: ref14 doi: 10.1109/jsen.2016.2516038 – ident: ref5 doi: 10.1117/1.oe.58.7.072007 – ident: ref6 doi: 10.1016/j.ifacol.2022.09.200 – ident: ref13 doi: 10.1364/ao.46.002205 – ident: ref3 doi: 10.1088/1742-6596/2293/1/012006 – ident: ref11 doi: 10.3390/s17102368 – ident: ref28 doi: 10.1016/s0030-3992(99)00071-7 – volume: 46 start-page: 530 issue: 5 year: 2020 ident: ref1 article-title: Research progress of intelligent algorithms for improving FBG sensing performance publication-title: Opt. Technique – ident: ref31 doi: 10.1109/jlt.2020.2971240 – ident: ref15 doi: 10.1088/1361-6501/aa637f – ident: ref7 doi: 10.1109/jlt.2016.2593260 – ident: ref20 doi: 10.1364/oe.413443 – ident: ref26 doi: 10.1109/tbme.2015.2468589 – ident: ref10 doi: 10.1016/s0030-4018(02)01907-7 – ident: ref8 doi: 10.3390/ma14051207 – ident: ref29 doi: 10.1109/jlt.2018.2879119 – ident: ref21 doi: 10.1109/jlt.2022.3155253 – ident: ref22 doi: 10.1109/jsen.2023.3238028 – year: 2014 ident: ref30 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980
SSID	ssj0019757
Score	2.4449074
Snippet	In quasi-distributed fiber Bragg grating (FBG) temperature sensor networks, noise and spectral distortions affect the demodulation accuracy of the fiber...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	10125
SubjectTerms	1-D convolutional neural network (1-DCNN) Accuracy Artificial neural networks Bragg gratings convolutional denoising autoencoder (CDAE) Demodulation fiber Bragg grating (FBG) Fiber gratings Goodness of fit Location awareness Machine learning Noise reduction Optical fiber sensors Recognition Reflection Root-mean-square errors Sensors Spectra spectral distortions Temperature temperature sensor networks Temperature sensors
Title	Recognition and Localization of FBG Temperature Sensing Based on Combined CDAE and 1-DCNN
URI	https://ieeexplore.ieee.org/document/10443347 https://www.proquest.com/docview/3031399095
Volume	24
WOSCitedRecordID	wos001245628200229&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-1748 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0019757 issn: 1530-437X databaseCode: RIE dateStart: 20010101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3faxQxEB7aUlAf2lornv1BHnwSUpPN7GXz2F7vWoosYiucT0s2O1FB9qS9Cv73JtlcaREF3_IwCct-O5mZnZlvAN5Y7VWh0HNTFiVHLxWvWpRcS5Lajm30UdOwCV3X1XxuPuRm9dQLQ0Sp-IyO4zLl8ruFu4u_yoKGIyqFeh3WtdZDs9Z9ysDoROsZNFhwVHqeU5hSmHeXV9M6hIIFHis1jgxbj4xQmqryx1Wc7Mts-z-fbAe2siPJTgbkn8Ma9bvw7AG94C48yRPOv_56AZ8_riqFFj2zfcfeRyOWmzDZwrPZ6Tm7puBEDyTL7CpWtvdf2Gkwcx0LMuHmCFF0WE_OTqbpCMnPJnW9B59m0-vJBc9jFbgrDC658iTbohpra9suhMcVBpeklaU3mspWOzICbVFIawtlkToiJGxb7xWOyUmhXsJGv-jpFTCndSfJdUK0BjtylaLgEQiUFMI2J_0IxOo9Ny5zjsfRF9-bFHsI00RomghNk6EZwdv7LT8Gwo1_Ce9FLB4IDjCM4GCFZpN18rZRkaYyGF9Tvv7Ltn14Gk8fCnMOYGN5c0eHsOl-Lr_d3hylz-03UpfPcQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB5BQSoceJQiFgr4wAnJxY_JOj62210KLBGii7ScIseZFCSURe0WiX-P7XirIgQSNx_GSZQv45nJzHwD8MKZTiuNHbeFKjh2UvOyQcmNJGnc2EUfNQ2bMFVVLpf2Q25WT70wRJSKz2g_LlMuv135i_irLGg4otZorsONAlHJoV3rMmlgTSL2DDosOGqzzElMKeyrtyfTKgSDCve1HkeOrd_MUJqr8sdhnCzM7O5_Pts9uJNdSXYwYH8frlG_A7evEAzuwHaecf7l5wP4_HFTK7TqmetbNo9mLLdhslXHZoev2YKCGz3QLLOTWNven7LDYOhaFmTC2RHi6LCeHB1M0yUkP5pU1S58mk0Xk2OeBytwryyuue5INqocG-eaNgTIJQanpJFFZw0VjfFkBTqlpHNKO6SWCAmbpus0jslLoR_CVr_q6REwb0wrybdCNBZb8qWm4BMIlBQCNy-7EYjNe659Zh2Pwy--1Sn6ELaO0NQRmjpDM4KXl1u-D5Qb_xLejVhcERxgGMHeBs06a-V5rSNRZTC_tnj8l23PYft48X5ez99U757ArXinoUxnD7bWZxf0FG76H-uv52fP0qf3C0gB0rg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Recognition+and+Localization+of+FBG+Temperature+Sensing+Based+on+Combined+CDAE+and+1-DCNN&rft.jtitle=IEEE+sensors+journal&rft.au=Jiang%2C+Hong&rft.au=Tang%2C+Rui&rft.au=Wang%2C+Chenyang&rft.au=Zhao%2C+Yihan&rft.date=2024-04-01&rft.issn=1530-437X&rft.eissn=1558-1748&rft.volume=24&rft.issue=7&rft.spage=10125&rft.epage=10137&rft_id=info:doi/10.1109%2FJSEN.2024.3365995&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JSEN_2024_3365995
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1530-437X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1530-437X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1530-437X&client=summon