用分层多粒度网络实现核电一二次回路故障的分级诊断
Research on fault diagnosis of nuclear power first-second circuit based on hierarchical multi-granularity classification network
- 基于EfficientNet构建分层多粒度分类模型，实现故障层级化识别
- 在模拟数据上实现不同回路与部件故障的有效分类
- 适合核电系统故障诊断研究者参考，尤其关注层级分析
核电厂复杂机电系统的安全可靠运行对核电生产安全至关重要，因此实现核电系统故障的精准及时诊断具有重要意义。现有方法多针对单一设备或子系统，难以分析全机组层面不同类型故障间的内在关联与相互影响。本文利用AP1000全尺寸模拟器，模拟核电一、二次回路关键系统中重要机械部件的故障，构建故障数据集，并提出一种基于EfficientNet大模型的分层多粒度分类故障诊断模型，旨在实现核电故障的层级分类。结果表明，所提模型能有效将核电机组不同回路及系统部件的故障分类至不同层级。但本研究中的故障数据来自模拟器，可能因参数冗余引入额外信息，从而影响模型诊断性能。
原文摘要 · Abstract (English)
The safe and reliable operation of complex electromechanical systems in nuclear power plants is crucial for the safe production of nuclear power plants and their nuclear power unit. Therefore, accurate and timely fault diagnosis of nuclear power systems is of great significance for ensuring the safe and reliable operation of nuclear power plants. The existing fault diagnosis methods mainly target a single device or subsystem, making it difficult to analyze the inherent connections and mutual effects between different types of faults at the entire unit level. This article uses the AP1000 full-scale simulator to simulate the important mechanical component failures of some key systems in the primary and secondary circuits of nuclear power units, and constructs a fault dataset. Meanwhile, a hierarchical multi granularity classification fault diagnosis model based on the EfficientNet large model is proposed, aiming to achieve hierarchical classification of nuclear power faults. The results indicate that the proposed fault diagnosis model can effectively classify faults in different circuits and system components of nuclear power units into hierarchical categories. However, the fault dataset in this study was obtained from a simulator, which may introduce additional information due to parameter redundancy, thereby affecting the diagnostic performance of the model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。