Abstract A predictive approach to fault diagnosis in complex systems such as the Nuclear power plant (NPP) is becoming popular because of the efficiency and accuracy it presents. However, there is still a huge gap between the proposed fault diagnosis techniques and engineering applications. To further optimize the fault diagnosis route and encourage real-time application, this paper presents a highly accurate and adaptable fault diagnosis technique based on the convolutional gated recurrent unit (CGRU) and enhanced particle swarm optimization (EPSO). Stacking convolutional kernel and GRU results in a model that speedily extract the local characteristics and learn the time-series information. The EPSO is utilized to adaptively search for optimal hyper-parameters for the CGRU. Finally, the accuracy is evaluated on a dataset obtained from experiments, and comparative analysis of the proposed model with existing architectures and models are presented. Relevant research results that show the usefulness of the proposed model are also presented, which highlights the enhanced intelligence and information level achieved in the NPP fault diagnosis.

中文翻译：

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基于卷积门控循环网络和增强粒子群优化的核电站先进故障诊断方法

摘要 核电站 (NPP) 等复杂系统中故障诊断的预测方法因其效率和准确性而变得流行。然而，所提出的故障诊断技术与工程应用之间仍存在巨大差距。为了进一步优化故障诊断路径并鼓励实时应用，本文提出了一种基于卷积门控循环单元（CGRU）和增强粒子群优化（EPSO）的高精度、适应性强的故障诊断技术。堆叠卷积核和 GRU 产生一个模型，可以快速提取局部特征并学习时间序列信息。EPSO 用于自适应搜索 CGRU 的最佳超参数。最后，在从实验获得的数据集上评估准确性，提出的模型与现有架构和模型的比较分析。还展示了表明所提出模型的有用性的相关研究结果，突出了核电厂故障诊断中实现的增强的智能和信息水平。