用改进卷积网络与麻雀算法结合，提升核电站故障诊断准确率。
Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm
- 融合改进时序卷积与麻雀优化算法，自动提取特征并调参。
- 在CPR1000仿真数据集上，各项指标均优于现有方法。
- 适合需要高可靠性的核电智能运维场景。
核电厂故障诊断对保障安全高效运行至关重要。传统方法依赖复杂特征提取与专家经验，存在耗时长、主观性强等问题。本文提出一种基于增强型时序卷积网络（ETCN）与麻雀搜索算法（SSA）融合的智能故障诊断方法。ETCN结合时序卷积网络（TCN）、自注意力（SA）机制与残差块，有效提取局部特征并捕捉时间序列信息；SSA则自适应优化ETCN超参数，提升模型性能。在CPR1000仿真数据集上的实验表明，该方法在所有评估指标上均优于其他先进智能诊断方法，展现出更强的诊断能力，为核电厂智能化故障诊断提供了有力工具，有助于提升运行可靠性。
原文摘要 · Abstract (English)
Utilizing fault diagnosis methods is crucial for nuclear power professionals to achieve efficient and accurate fault diagnosis for nuclear power plants (NPPs). The performance of traditional methods is limited by their dependence on complex feature extraction and skilled expert knowledge, which can be time-consuming and subjective. This paper proposes a novel intelligent fault diagnosis method for NPPs that combines enhanced temporal convolutional network (ETCN) with sparrow search algorithm (SSA). ETCN utilizes temporal convolutional network (TCN), self-attention (SA) mechanism and residual block for enhancing performance. ETCN excels at extracting local features and capturing time series information, while SSA adaptively optimizes its hyperparameters for superior performance. The proposed method's performance is experimentally verified on a CPR1000 simulation dataset. Compared to other advanced intelligent fault diagnosis methods, the proposed one demonstrates superior performance across all evaluation metrics. This makes it a promising tool for NPP intelligent fault diagnosis, ultimately enhancing operational reliability.
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