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

Autor: Zekun Wu, Mohamed A. S. Zaghloul, David Carpenter, Ming-Jun Li, Joshua Daw, Zhi-Hong Mao, Cyril Hnatovsky, Stephen J. Mihailov, Kevin P. Chen
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: IEEE Access, Vol 9, Pp 148296-148301 (2021)
Druh dokumentu: article
ISSN: 2169-3536
71644148
DOI: 10.1109/ACCESS.2021.3124860
Popis: 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.
Databáze: Directory of Open Access Journals
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