Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network

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 fi...

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Autores principales:	Zekun Wu, Mohamed A. S. Zaghloul, David Carpenter, Ming-Jun Li, Joshua Daw, Zhi-Hong Mao, Cyril Hnatovsky, Stephen J. Mihailov, Kevin P. Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Fiber Bragg grating (FBG) long short-term memory (LSTM) network radiation effects reactor anomaly identification supervised learning sensor drifts mitigation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fe81c990b71644148471a8a200e43511
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spelling	oai:doaj.org-article:fe81c990b71644148471a8a200e435112021-11-18T00:07:27ZMitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network2169-353610.1109/ACCESS.2021.3124860https://doaj.org/article/fe81c990b71644148471a8a200e435112021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598896/https://doaj.org/toc/2169-3536This 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.Zekun WuMohamed A. S. ZaghloulDavid CarpenterMing-Jun LiJoshua DawZhi-Hong MaoCyril HnatovskyStephen J. MihailovKevin P. ChenIEEEarticleFiber Bragg grating (FBG)long short-term memory (LSTM) networkradiation effectsreactor anomaly identificationsupervised learningsensor drifts mitigationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 148296-148301 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Fiber Bragg grating (FBG) long short-term memory (LSTM) network radiation effects reactor anomaly identification supervised learning sensor drifts mitigation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Fiber Bragg grating (FBG) long short-term memory (LSTM) network radiation effects reactor anomaly identification supervised learning sensor drifts mitigation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zekun Wu Mohamed A. S. Zaghloul David Carpenter Ming-Jun Li Joshua Daw Zhi-Hong Mao Cyril Hnatovsky Stephen J. Mihailov Kevin P. Chen Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network
description	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.
format	article
author	Zekun Wu Mohamed A. S. Zaghloul David Carpenter Ming-Jun Li Joshua Daw Zhi-Hong Mao Cyril Hnatovsky Stephen J. Mihailov Kevin P. Chen
author_facet	Zekun Wu Mohamed A. S. Zaghloul David Carpenter Ming-Jun Li Joshua Daw Zhi-Hong Mao Cyril Hnatovsky Stephen J. Mihailov Kevin P. Chen
author_sort	Zekun Wu
title	Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network
title_short	Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network
title_full	Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network
title_fullStr	Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network
title_full_unstemmed	Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network
title_sort	mitigation of radiation-induced fiber bragg grating (fbg) sensor drifts in intense radiation environments based on long-short-term memory (lstm) network
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fe81c990b71644148471a8a200e43511
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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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