Mitigation of radiation-induced fiber Bragg grating (FBG) sensor drifts in intense radiation environments based on long-short-term memory (LSTM) network

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DOI	Trouver le DOI : https://doi.org/10.1109/ACCESS.2021.3124860
Auteur	Rechercher : Wu, Zekun Identifiant ORCID : https://orcid.org/0000-0003-0131-7352; Rechercher : Zaghloul, Mohamed A. S.; Rechercher : Carpenter, David; Rechercher : Li, Ming-Jun Identifiant ORCID : https://orcid.org/0000-0003-4141-6670; Rechercher : Daw, Joshua Identifiant ORCID : https://orcid.org/0000-0003-4377-6231; Rechercher : Mao, Zhi-Hong Identifiant ORCID : https://orcid.org/0000-0002-3025-463X; Rechercher : Hnatovsky, Cyril¹Identifiant ORCID : https://orcid.org/0000-0003-1838-9643; Rechercher : Mihailov, Stephen J.¹Identifiant ORCID : https://orcid.org/0000-0002-6144-0937; Rechercher : Chen, Kevin P.Identifiant ORCID : https://orcid.org/0000-0003-1595-4475
Affiliation	Conseil national de recherches Canada. Technologies de sécurité et de rupture
Format	Texte, Article
Sujet	fiber Bragg grating (FBG); long short-term memory (LSTM) network; radiation effects; reactor anomaly identification; supervised learning; sensor drifts mitigation; temperature measurement; wavelength-shift
Résumé	This paper reports in-pile testing results of radiation-resistant fiber Bragg grating (FBG) sensors at high temperatures, intense neutron irradiation environments, and machine learning methods for radiation-induced sensor drift mitigation and reactor anomaly identification. The in-pile testing of fiber sensors was carried out in an MIT test reactor for 180 days at a nominal operational temperature of 640°C and high neutron flux. The test results show that FBG sensors inscribed by a femtosecond laser in random airline pure silica fiber can withstand harsh environments in the reactor core but exhibit significant radiation-induced drifts. Machine learning algorithms based on long short-term memory (LSTM) networks have been used to detect reactor anomaly events and mitigate sensor drifts over a duration of up to 85 days. Through progressive supervised learning, the LSTM neural network can achieve FBG wavelength-to-temperature mapping within ±0.95°C, ±2.63°C and ±6.49°C with over 80.2%, 90%, and 95% levels of accuracy confidence, respectively. The LSTM can also identify reactor anomaly samples with an accuracy of over 94%. The results presented in this paper show that despite sensor drifts and anomaly interruptions, the LSTM-based method can effectively elucidate data harnessed by fiber sensors. Machine learning algorithms have the potential to improve situational awareness and control for a wide range of harsh environment applications, including nuclear power generation.
Date de publication	2021-11-08
Maison d’édition	IEEE
Licence	Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.fr
Dans	IEEE Access 9 (8 novembre 2021) : 148296–148301.
Langue	anglais
Publications évaluées par des pairs	Oui
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	f896cd35-7b64-4118-a037-53b5edc0e81e
Enregistrement créé	2022-02-18
Enregistrement modifié	2022-02-23

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Date de modification :: 2026-07-13