Academic Journal

MobileViT-based Detection of Anomaly in Measurements of Nuclear Power Plant Core Temperature

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: MobileViT-based Detection of Anomaly in Measurements of Nuclear Power Plant Core Temperature
Συγγραφείς: Cogranne, Rémi
Συνεισφορές: Cogranne, Rémi, IEEE
Πηγή: 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS). :502-508
Στοιχεία εκδότη: IEEE, 2025.
Έτος έκδοσης: 2025
Θεματικοί όροι: [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
Περιγραφή: 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.
Τύπος εγγράφου: Article
Conference object
Περιγραφή αρχείου: application/pdf
DOI: 10.1109/amlds63918.2025.11159353
Σύνδεσμος πρόσβασης: https://hal.science/hal-05016499v1
Rights: STM Policy #29
Αριθμός Καταχώρησης: edsair.doi.dedup.....b225a367ecf6e7445087a3eda2ec764d
Βάση Δεδομένων: OpenAIRE
Περιγραφή
DOI:10.1109/amlds63918.2025.11159353