用贝叶斯+EfficientNet实现核电厂多故障智能诊断
A Composite Fault Diagnosis Model for NPPs Based on Bayesian-EfficientNet Module
- 融合贝叶斯优化与EfficientNet的复合诊断模型
- 基于迁移学习实现多系统故障精准识别
- 适合核电厂设备智能运维人员参考
本文聚焦于核电厂反应堆冷却系统、主蒸汽系统、凝结水系统及主给水系统的泵、阀、管道等关键机械部件的故障问题，提出一种基于贝叶斯算法与EfficientNet大模型的复合多故障诊断模型。该模型采用数据驱动的深度学习故障诊断技术，旨在通过迁移学习评估大规模深度学习模型在核电厂场景下的有效性，提升复杂系统中多种故障的自动识别能力。
原文摘要 · Abstract (English)
This article focuses on the faults of important mechanical components such as pumps, valves, and pipelines in the reactor coolant system, main steam system, condensate system, and main feedwater system of nuclear power plants (NPPs). It proposes a composite multi-fault diagnosis model based on Bayesian algorithm and EfficientNet large model using data-driven deep learning fault diagnosis technology. The aim is to evaluate the effectiveness of automatic deep learning-based large model technology through transfer learning in nuclear power plant scenarios.
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