基于神经正常随机过程的核电生产中的异常检测
Linyu Liu1, Shiqiao Liu1,2, Shuan He1
1China Nuclear Power Operation Technology Corporation, Wuhan 430233, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
核电厂使用传感器来检测异常,但数据丢失是一个挑战. 一种新的神经正常静态过程 (NNSP) 方法有效地检测不完整的传感器数据中的异常.
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:
- 核电厂利用广泛的传感器网络进行安全监测和异常检测.
- 传感器数据完整性对于有效的异常检测至关重要,但由于恶劣的操作环境,通常会受到损害.
- 不完整的传感器数据对传统的异常检测方法构成重大挑战.
研究的目的:
- 为核能数据开发一种新的异常检测方法,可以处理缺失的传感器读数.
- 引入神经正常静态过程 (NNSP) 模型,旨在直接处理不完整的时间序列数据.
- 评估NNSP的性能与现实世界核电数据的既定异常检测技术对比.
主要方法:
- 提出了神经正常静态过程 (NNSP),一种使用序列化结构的深度学习方法.
- 将不完整的传感器数据编码为神经网络中的连续隐藏表示.
- 训练模型通过指定缺失或未来时间点的监督信号来识别异常模式.
主要成果:
- 在涉及不完整时间序列数据的异常检测任务中,NNSP表现出卓越的性能.
- 在15%缺失数据的发电系统 (PGS) 数据集中,NNSP获得了83.72%的F1得分.
- 该模型的表现优于五种主流基线方法,包括ARMA,隔离森林,LSTM-AD,VAE和中性ADL.
结论:
- 该NNSP方法有效地解决了在核电异常检测中缺少传感器数据的挑战.
- NNSP提供了一个强大的数据归算替代方案,直接从不完整的监控数据中学习.
- 该模型显示了提高核电运营的安全性和可靠性的巨大潜力.
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