Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Lin, Weiqing; Miao, Xiren; Chen, Jing; Ye, Mingxin; Xu, Yong; Liu, Xinyu; Jiang, Hao; Lu, Yanzhen

doi:10.1016/j.knosys.2024.112182

Knowledge-Based Systems

2024

DOI: 10.1016/j.knosys.2024.112182

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Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Weiqing Lin,

Xiren Miao,

Jing Chen

et al.

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2024

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Cited by 2 publications

References 41 publications

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Application of SPEA2-MMBB for Distributed Fault Diagnosis in Nuclear Power System

Xu,

Ma,

Yuan

2024

Processes

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Accurate fault diagnosis in nuclear power systems is essential for ensuring reactor stability, reducing the risk of potential faults, enhancing system reliability, and maintaining operational safety. Traditional diagnostic methods, especially those based on single-system approaches, struggle to address the complexities of composite faults and highly coupled fault data. In this paper, we introduce a distributed fault diagnosis method for nuclear power systems that leverages the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for multi-objective optimization and a modified MobileNetV3 neural network with a Bottleneck Attention Module (MMBB). The SPEA2 algorithm is used to optimize sensor feature selection, and the sensor data are then input into the MMBB model for training. The MMBB model outputs accuracy rates for each subsystem and the overall system, which are subsequently used as optimization targets to guide SPEA2 in refining the sensor selection process for distributed diagnosis. The experimental results demonstrate that this method significantly enhances subsystem accuracy, with an average accuracy of 98.73%, and achieves a comprehensive system accuracy of 95.22%, indicating its superior performance compared to traditional optimization and neural network-based approaches.

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Application of SPEA2-MMBB for Distributed Fault Diagnosis in Nuclear Power System

Xu,

Ma,

Yuan

2024

Processes

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Tool State Recognition Based on POGNN-GRU under Unbalanced Data

Tong,

Shen,

et al. 2024

Sensors

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Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model.

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Fault detection and isolation for multi-type sensors in nuclear power plants via a knowledge-guided spatial–temporal model

Cited by 2 publications

References 41 publications

Application of SPEA2-MMBB for Distributed Fault Diagnosis in Nuclear Power System

Application of SPEA2-MMBB for Distributed Fault Diagnosis in Nuclear Power System

Tool State Recognition Based on POGNN-GRU under Unbalanced Data

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