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首页> 外文会议>Second International Conference on Advanced Computing, Networking and Security >Anomaly Detection and Similarity Search in Neutron Monitor Data for Predictive Maintenance of Nuclear Power Plants
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Anomaly Detection and Similarity Search in Neutron Monitor Data for Predictive Maintenance of Nuclear Power Plants

机译：中子监测器数据中的异常检测和相似性搜索，用于核电站的预测维护

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摘要

Anomaly detection and similarity search in time series data is an area of wide research in the field of data mining. In this paper we introduce a nearest neighbor based technique for performing anomaly detection over time series data. It is based on the observation that any anomalous behavior is surrounded by a large variation in slope of the graph obtained by plotting the time sequence. Time series comprising of the count of delayed neutrons have been analyzed for the purpose of predictive maintenance in nuclear power plants. We aim to identify anomalies in the neutron counts possibly due to leaks in the nuclear reactor channel.
机译：时间序列数据中的异常检测和相似性搜索是数据挖掘领域中广泛研究的领域。在本文中，我们介绍了一种用于基于时间序列数据执行异常检测的基于最近邻居的技术。基于这样的观察，任何异常行为都被通过绘制时间序列而获得的图的斜率的大变化所包围。为了预测性维护核电站，已经分析了由延迟中子计数组成的时间序列。我们旨在识别可能由于核反应堆通道泄漏而引起的中子计数异常。

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