边缘时间数字双胞胎网络用于核电系统的传感器驱动故障检测
Shiqiao Liu1,2, Gang Ye3, Xinwen Zhao1
1College of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了用于核电系统的边缘时间数字双胞胎网络 (ETDTN). 通过暂时分析传感器数据,在保护数据隐私的同时,ETDTN增强了故障检测.
科学领域:
- 核工程 核工程是指核工程.
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 核电系统依靠传感器网络来确保安全运行.
- 数据共享限制阻碍了通用数字双胞胎的发展.
- 现有的数字双胞胎往往忽视时间传感器数据模式,这对于故障预测至关重要.
研究的目的:
- 提出一个边缘时间数字双胞胎网络 (ETDTN),用于核电系统的云边缘协作故障检测.
- 解决核传感器网络中数据隐私和非IID数据的挑战.
- 通过将来自传感器数据的时间信息纳入,改进故障预测.
主要方法:
- 开发了一个边缘时间数字双胞胎网络 (ETDTN),使用连续变量时间表示.
- 整合了一个全球表示模块,用于跨子系统处理非IID数据.
- 集成了一个时间注意力机制与图形神经网络,以增强时间特征学习.
- 采用联合参数聚合来确保数据隐私.
主要成果:
- 与现有方法相比,ETDTN在非共享数据场景中显示出明显优越的故障检测性能.
- 在真实核能数据集上,在准确性和F1得分方面取得了最先进的结果.
- 验证了时间特征学习和隐私保护的有效性.
结论:
- 通过联合学习,ETDTN有效地保护数据隐私.
- 该网络成功地捕获传感器数据中的潜在时间模式,以改进故障检测.
- ETDTN为核电系统的传感器驱动故障检测和预测性维护提供了强大的解决方案.
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