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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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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