Academic Journal

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

Bibliographic Details
Title: MobileViT-based Detection of Anomaly in Measurements of Nuclear Power Plant Core Temperature
Authors: Cogranne, Rémi
Contributors: Cogranne, Rémi, IEEE
Source: 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS). :502-508
Publisher Information: IEEE, 2025.
Publication Year: 2025
Subject Terms: [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
Description: 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.
Document Type: Article
Conference object
File Description: application/pdf
DOI: 10.1109/amlds63918.2025.11159353
Access URL: https://hal.science/hal-05016499v1
Rights: STM Policy #29
Accession Number: edsair.doi.dedup.....b225a367ecf6e7445087a3eda2ec764d
Database: OpenAIRE
Description
DOI:10.1109/amlds63918.2025.11159353