Unsupervised anomaly detection in pressurized water reactor digital twins using autoencoder neural networks

Cancemi, Salvatore Angelo; Frano, Rosa Lo; Santus, Ciro; Inoue, Tomoya

doi:10.1016/j.nucengdes.2023.112502

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…The DT is constructed and calibrated autonomously for the NPPs core (Li et al, 2023d). It is also employed in nuclear reactors for parameter identification and state estimation (Gong et al, 2023), and anomaly detection (Cancemi et al, 2023).…”

Section: Big Data and Ai Applications In Nppsmentioning

confidence: 99%

Application of artificial intelligence technologies and big data computing for nuclear power plants control: a review

Ejigu,

Tuo,

Liu

2024

Front. Nucl. Eng.

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Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.

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Section: Big Data and Ai Applications In Nppsmentioning

confidence: 99%