Combinatorial Techniques for Fault Diagnosis in Nuclear Power Plants Based on Bayesian Neural Network and Simplified Bayesian Network-Artificial Neural Network

Qi, Ben; Zhang, Liguo; Liang, Jingang; Tong, Jiejuan

doi:10.3389/fenrg.2022.920194

Cited by 10 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data-driven methods for fault diagnosis in NPPs have attracted increasing interest in recent years. They include artificial neural networks (ANNs) [150,153,154], support vector machines (SVMs), decision trees (DTs), principal component analysis (PCA), and clustering [155]. Certainly, numerous studies have also opted for hybrid algorithms [155].…”

Section: Fault Diagnosismentioning

confidence: 99%

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Xiao,

Liang,

Tong

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

Emergency decision support techniques play an important role in complex and safety-critical systems such as nuclear power plants (NPPs). Emergency decision-making is not a single method but a framework comprising a combination of various technologies. This paper presents a review of various methods for emergency decision support systems in NPPs. We first discuss the theoretical foundations of nuclear power plant emergency decision support technologies. Based on this exposition, the key technologies of emergency decision support systems in NPPs are presented, including training operators in emergency management, risk assessment, fault detection and diagnosis, multi-criteria decision support, and accident consequence assessment. The principles, application, and comparative analysis of these methods are systematically described. Additionally, we present an overview of emergency decision support systems in NPPs across different countries and feature profiles of prominent systems like the Real-Time Online Decision Support System for Nuclear Emergencies (RODOS), the Accident Reporting and Guiding Operational System (ARGOS), and the Decision Support Tool for Severe Accidents (Severa). Then, the existing challenges and issues in this field are summarized, including the need for better integration of risk assessment, methods to enhance education and training, the acceleration of simulation calculations, the application of large language models, and international cooperation. Finally, we propose a new decision support system that integrates Level 1, 2, and 3 probabilistic safety assessment for emergency management in NPPs.

show abstract

Section: Fault Diagnosismentioning

confidence: 99%

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Xiao,

Liang,

Tong

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Qi et al . used the simulator of three-loop pressurised water reactor to validate hybrid AI algorithms driven by both knowledge and data 14 . Wang et al .…”

Section: Background and Summarymentioning

confidence: 99%

An open time-series simulated dataset covering various accidents for nuclear power plants

Xiao

Liang

et al. 2022

Sci Data

Self Cite

View full text Add to dashboard Cite

Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.

show abstract

“…Nuclear power plants (NPPs) consist of multiple intricate, nonlinear, and dynamic systems. The availability of large amounts of information from operators, due to advances in digital technology [1], has made it challenging to swiftly diagnose fault information. Furthermore, research has established human error as the primary cause of accidents in NPPs [2], in particular, the intrinsic human factors of uncertainty [3] and the impact of human-computer interface design [4].…”

Section: Introductionmentioning

confidence: 99%

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

Xiao,

Qi,

Liang

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Combinatorial Techniques for Fault Diagnosis in Nuclear Power Plants Based on Bayesian Neural Network and Simplified Bayesian Network-Artificial Neural Network

Cited by 10 publications

References 40 publications

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends

An open time-series simulated dataset covering various accidents for nuclear power plants

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

Contact Info

Product

Resources

About