Abstract Accurate online condition monitoring and anomaly detection are crucial in nuclear applications to optimize economic performance and minimize safety risks. To achieve this goal, several major challenges exist which must be addressed. First, multi-sensor signals are often collected in the form of complex, multivariate time series. Second, relatively few anomaly records are available to train detection models. Lastly, the recorded data may contain uncertainties resulting from various sources, such as operator-induced variability and measurement error. In this paper, a recurrent neural network-based approach is proposed to tackle these issues by effectively utilizing historical data obtained during both normal and abnormal operations. Several advanced data preprocessing techniques are developed to improve the training process of the proposed neural network. The efficiency and sensitivity of the proposed method are evaluated on the multi-sensor signal measurements and operational reports obtained from a real case study. The results demonstrate much improved detection accuracy and practicality of the proposed method over conventional approaches.

中文翻译：

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基于 RNN 的核反应堆在线异常检测，用于具有不确定性的高度不平衡数据集

摘要 在核应用中，准确的在线状态监测和异常检测对于优化经济性能和最小化安全风险至关重要。为了实现这一目标，必须解决几个主要挑战。首先，多传感器信号通常以复杂的多元时间序列的形式收集。其次，可用于训练检测模型的异常记录相对较少。最后，记录的数据可能包含由各种来源引起的不确定性，例如操作员引起的可变性和测量误差。在本文中，提出了一种基于循环神经网络的方法，通过有效利用在正常和异常操作期间获得的历史数据来解决这些问题。开发了几种先进的数据预处理技术来改进所提出的神经网络的训练过程。所提出方法的效率和灵敏度是根据从实际案例研究中获得的多传感器信号测量和操作报告来评估的。结果表明，与传统方法相比，所提出的方法的检测精度和实用性大大提高。