MobileViT-based Detection of Anomaly in Measurements of Nuclear Power Plant Core Temperature
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| Název: | MobileViT-based Detection of Anomaly in Measurements of Nuclear Power Plant Core Temperature |
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| Autoři: | Cogranne, Rémi |
| Přispěvatelé: | Cogranne, Rémi, IEEE |
| Zdroj: | 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS). :502-508 |
| Informace o vydavateli: | IEEE, 2025. |
| Rok vydání: | 2025 |
| Témata: | [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Artificial intelligence, Image processing, CUSUM, Sequential methods, Empirical evaluation, Anomaly detection, Supervised learning, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR], [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing |
| Popis: | This paper presents a simple model based on MobileViT-v2 for temperature monitoring within a nuclear power plant. Specifically, it is proposed to use MobileNet-v2 to detect a critical accident: a total and instantaneous blockage. On the one hand, the temperature effects of such an event is modeled ans a MobileViT-v2 model for detection. The trained classifier's results are then used in a sequential procedure to detect blockage as quickly and reliably as possible. We compare the performance of two sequential detection methods, namely slidingwindow and CUSUM, in terms of mean detection delay and probability of detection before a prescribed maximum detection delay. Experimental results, using actual temperature measurements from the Superphénix power station, demonstrate the effectiveness of the proposed detection method. |
| Druh dokumentu: | Article Conference object |
| Popis souboru: | application/pdf |
| DOI: | 10.1109/amlds63918.2025.11159353 |
| Přístupová URL adresa: | https://hal.science/hal-05016499v1 |
| Rights: | STM Policy #29 |
| Přístupové číslo: | edsair.doi.dedup.....b225a367ecf6e7445087a3eda2ec764d |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | This paper presents a simple model based on MobileViT-v2 for temperature monitoring within a nuclear power plant. Specifically, it is proposed to use MobileNet-v2 to detect a critical accident: a total and instantaneous blockage. On the one hand, the temperature effects of such an event is modeled ans a MobileViT-v2 model for detection. The trained classifier's results are then used in a sequential procedure to detect blockage as quickly and reliably as possible. We compare the performance of two sequential detection methods, namely slidingwindow and CUSUM, in terms of mean detection delay and probability of detection before a prescribed maximum detection delay. Experimental results, using actual temperature measurements from the Superphénix power station, demonstrate the effectiveness of the proposed detection method. |
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| DOI: | 10.1109/amlds63918.2025.11159353 |