Mitigation of Radiation-Induced Fiber Bragg Grating (FBG) Sensor Drifts in Intense Radiation Environments Based on Long-Short-Term Memory (LSTM) Network
| Autor: | Kevin P. Chen, Zekun Wu, David Carpenter, Cyril Hnatovsky, Mohamed S. Zaghloul, Joshua Daw, Ming-Jun Li, Stephen J. Mihailov, Zhi-Hong Mao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
reactor anomaly identification
Optical fiber General Computer Science Silica fiber Artificial neural network Computer science Supervised learning General Engineering wavelength-shift Temperature measurement supervised learning law.invention TK1-9971 sensor drifts mitigation Fiber Bragg grating Nuclear reactor core law Neutron flux Fiber Bragg grating (FBG) Electronic engineering radiation effects General Materials Science Electrical engineering. Electronics. Nuclear engineering temperature measurement long short-term memory (LSTM) network |
| Zdroj: | IEEE Access, Vol 9, Pp 148296-148301 (2021) |
| ISSN: | 2169-3536 |
| 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: | OpenAIRE |
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