Fault supervision of nuclear research reactor systems using artificial neural networks: A review with results

Khentout, Nourddine; Magrotti, Giovanni

doi:10.1016/j.anucene.2023.109684

Cited by 5 publications

(3 citation statements)

References 102 publications

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“…Further, these modeling mechanisms are used to design the core fuel assembly of the research reactor automatically (Kim et al, 2020). More, they are employed for optimization and burnup calculations of the reactor core (Afzali et al, 2022) as well as for the NPPs fault supervision (Khentout and Magrotti, 2023). The ANNs are suitable and effective mechanisms to diagnose transients of a nuclear reactor during operation and to improve safety (Santosh et al, 2007).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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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: Artificial Neural Networkmentioning

confidence: 99%

“…Application of AI algorithms and big data computing (Shukla et al, 2019;Yüksel et al, 2023). Khentout and Magrotti (2023) Frontiers in Nuclear Engineering frontiersin.org…”

Section: Figure 13mentioning

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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“…The MTS data, sourced from diverse sensor observations, are subject to temporal fluctuations and exhibit pronounced cross-channel coupling due to intrinsic mechanistic interrelations. The application of deep learning, celebrated for its remarkable prowess in feature dimensionality reduction and extraction [8,9], to anomaly detection of complex, nonlinear, and intricately coupled systems like NPPs, is imbued with significant potential [10]. Nonetheless, there exist several challenges that necessitate comprehensive consideration and resolution.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection for Asynchronous Multivariate Time Series of Nuclear Power Plants Using a Temporal-Spatial Transformer

Yi,

Zheng,

Yang

et al. 2024

Sensors

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Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs.

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Enhancing prediction accuracy for LOCA break sizes in nuclear power plants: A hybrid deep learning method with data augmentation and hyperparameter optimization

Zhou,

Peng,

Wang

2024

Annals of Nuclear Energy

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Fault supervision of nuclear research reactor systems using artificial neural networks: A review with results

Cited by 5 publications

References 102 publications

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

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

Anomaly Detection for Asynchronous Multivariate Time Series of Nuclear Power Plants Using a Temporal-Spatial Transformer

Enhancing prediction accuracy for LOCA break sizes in nuclear power plants: A hybrid deep learning method with data augmentation and hyperparameter optimization

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