Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective

Qi, Ben; Liang, Jingang; Tong, Jiejuan

doi:10.3390/en16041850

Cited by 15 publications

(8 citation statements)

References 125 publications

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“…Several recent studies have highlighted the potential of AI in radiation protection. For example, a study conducted by Qi et al demonstrated the effectiveness of AI-based algorithms in detecting and predicting radiation exposure levels in nuclear power plants [ 4 ]. Additionally, a review article by Abolaban (2023) discussed the application of AI in optimizing radiation therapy delivery for cancer patients, leading to improved treatment outcomes and reduced side effects [ 5 ].…”

Section: Dear Editormentioning

confidence: 99%

Artificial Intelligence (AI); A Revolution in Radiation Protection in Modern Life

2024

J Biomed Phys Eng

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Section: Dear Editormentioning

confidence: 99%

Artificial Intelligence (AI); A Revolution in Radiation Protection in Modern Life

2024

J Biomed Phys Eng

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“…With the progress of deep learning (DL), several hybrid models composed of multiple deep learning models for diagnosis have been successfully applied in the nuclear fault diagnosis field [7]. As shown in Table 2, She et al combined CNN, LSTM, and convolutional LSTM (ConvLSTM) for the diagnosis and prediction of LOCAs, and this hybrid model has been proven to be functional, accurate, and divisible [8].…”

Section: Related Workmentioning

confidence: 99%

Enhancing LOCA Breach Size Diagnosis with Fundamental Deep Learning Models and Optimized Dataset Construction

Xiao,

Qi,

Liang

et al. 2023

Energies

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In nuclear power plants, the loss-of-coolant accident (LOCA) stands out as the most prevalent and consequential incident. Accurate breach size diagnosis is crucial for the mitigation of LOCAs, and identifying the cause of an accident can prevent catastrophic consequences. Traditional methods mostly focus on combining model algorithms and utilize intricate composite model neural network architectures. However, it is crucial to investigate whether greater complexity necessarily leads to better performance. In addition, the consideration of the impact of dataset construction and data preprocessing on model performance is also needed for model building. This paper proposes a framework named DeepLOCA-Lattice to experiment with different preprocessing approaches to fundamental deep learning models for a comprehensive analysis of the diagnosis of LOCA breach size. The DeepLOCA-Lattice involves data preprocessing via the lattice algorithm and equal-interval partitioning and deep-learning-based models, including the multi-layer perceptron (MLP), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and the transformer model in LOCA breach size diagnosis. After conducting rigorous ablation experiments, we have discovered that even rudimentary foundational models can achieve accuracy rates that exceed 90%. This is a significant improvement when compared to the previous models, which yield an accuracy rate of lower than 50%. The results interestingly demonstrate the superior performance and efficacy of the fundamental deep learning model, with an effective dataset construction approach. It elucidates the presence of a complex interplay among diagnostic scales, sliding window size, and sliding stride. Furthermore, our investigation reveals that the model attains its highest accuracy within the discussed range when utilizing a smaller sliding stride size and a longer sliding window length. This study could furnish valuable insights for constructing models for LOCA breach size estimation.

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“…mat format. Click on the converted file in the workspace to obtain the corresponding data [6][7][8]. Be sure to save the converted file to the correct folder.…”

Section: Fault Diagnosis Steps Based On Som Neural Networkmentioning

confidence: 99%

Fault Diagnosis of Hydraulic System based on SOM Neural Network

Chen¹,

Xiao²,

Li³

et al. 2023

FCIS

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The working principle of hydraulic system is to use the flow and pressure of liquid in the system for energy transfer and conversion. Hydraulic system realizes various work tasks such as pushing, grasping, lifting, rotating, etc. by controlling the action of hydraulic actuators. Its advantages are high power density, good stability, fast response time, smooth power output, etc. The disadvantage is that the hydraulic system is easy to lose control of more points. In this paper, taking the horizontal outrigger hydraulic circuit as an example, the hydraulic pump leakage, etc. is used as a fault sample, and the SOM neural network is used for fault diagnosis to make timely and accurate diagnosis of the abnormal or fault state of the hydraulic system, give guidance on the operation of the hydraulic system, improve the reliability and safety of operation, and reduce the fault loss to a minimum.

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Fault Diagnosis Techniques for Nuclear Power Plants: A Review from the Artificial Intelligence Perspective

Cited by 15 publications

References 125 publications

Artificial Intelligence (AI); A Revolution in Radiation Protection in Modern Life

Artificial Intelligence (AI); A Revolution in Radiation Protection in Modern Life

Enhancing LOCA Breach Size Diagnosis with Fundamental Deep Learning Models and Optimized Dataset Construction

Fault Diagnosis of Hydraulic System based on SOM Neural Network

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