基于双注意力自编码器的核工业时间序列数据可解释无监督多异常检测与时序定位
Explainable Unsupervised Multi-Anomaly Detection and Temporal Localization in Nuclear Times Series Data with a Dual Attention-Based Autoencoder
AI总结：
提出基于双注意力LSTM自编码器的无监督框架，用于核反应堆辐射监测系统中的多异常检测与时序定位，通过特征和时间维度的注意力机制实现可解释的传感器与异常持续时间识别。
摘要：
核工业正朝着更多新反应堆设计方向发展，下一代反应堆预计在规模和功率输出上更小。这些系统有可能以多变量时间序列数据的形式产生大量信息，可用于增强实时监控与控制。在此背景下，用于反应堆运行的远程自主或半自主控制系统的开发引起了广泛关注。此类系统的关键第一步是开发一个精确的诊断模块，能够检测并定位反应堆系统内的异常。近期研究提出了多种用于核领域异常检测的ML和DL方法。尽管结果令人鼓舞，但关键挑战依然存在，包括可解释性有限甚至没有、缺乏对真实世界数据的访问，以及异常事件稀缺，这阻碍了基准测试和特征表征。大多数现有研究将这些方法视为黑盒，而近期工作强调在安全关键领域需要更高的ML/DL输出可解释性。在此，我们提出了一种基于带有双注意力机制的LSTM自编码器的无监督方法，用于表征真实世界反应堆辐射区域监测系统中的异常事件。该框架不仅包括检测，还包括事件定位，并使用来自PUR-1研究反应堆的复杂度递增的真实世界数据集进行了评估。注意力机制在特征和时间维度上均起作用，其中特征注意力对表现出异常模式的辐射传感器分配权重，而时间注意力突出发生不规则性的特定时间步，从而实现定位。通过结合结果，该框架能够在单个统一网络中识别受影响的传感器和每个异常的持续时间。
英文摘要：
The nuclear industry is advancing toward more new reactor designs, with next-generation reactors expected to be smaller in scale and power output. These systems have the potential to produce large volumes of information in the form of multivariate time-series data, which could be used for enhanced real-time monitoring and control. In this context, the development of remote autonomous or semi-autonomous control systems for reactor operation has gained significant interest. A critical first step toward such systems is an accurate diagnostics module capable of detecting and localizing anomalies within the reactor system. Recent studies have proposed various ML and DL approaches for anomaly detection in the nuclear domain. Despite promising results, key challenges remain, including limited to no explainability, lack of access to real-world data, and scarcity of abnormal events, which impedes benchmarking and characterization. Most existing studies treat these methods as black boxes, while recent work highlights the need for greater interpretability of ML/DL outputs in safety-critical domains. Here, we propose an unsupervised methodology based on an LSTM autoencoder with a dual attention mechanism for characterization of abnormal events in a real-world reactor radiation area monitoring system. The framework includes not only detection but also localization of the event and was evaluated using real-world datasets of increasing complexity from the PUR-1 research reactor. The attention mechanisms operate in both the feature and temporal dimensions, where the feature attention assigns weights to radiation sensors exhibiting abnormal patterns, while time attention highlights the specific timesteps where irregularities occur, thus enabling localization. By combining the results, the framework can identify both the affected sensors and the duration of each anomaly within a single unified network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
