Abstract Savannah River National Laboratory (SRNL) has established an In-Situ Decommissioning (ISD) Sensor Network Test Bed—a unique, small scale, and configurable environment— for the assessment of prospective sensors on actual ISD system at minimal cost. The temporal data mining (TDM) technique can be employed to process the extensive data collected by the ISD sensors well because these data are time-specific, age-specific, and development stage-specific. This paper analyzed the baseline data collected by ISD Sensor Network Test Bed in recent years with the assistant of TDM algorithms to work out frequency episodes in the event stream. The results have confirmed that TDM techniques are effective tools to validate ISD performance, and the frequent episodes found in the data stream not only showed the daily cycle of the sensor responses, but also established the response sequences of different types of sensors, which was verified by the actual experimental setup. Some abnormal patterns may have the potential for prediction of system failures.

中文翻译：

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使用时态数据挖掘工具寻找原位退役 (ISD) 传感器网络试验台基线数据中的频繁事件

摘要 萨凡纳河国家实验室 (SRNL) 建立了一个原位退役 (ISD) 传感器网络测试平台——一个独特的、小规模的、可配置的环境——用于以最低成本评估实际 ISD 系统上的预期传感器。时间数据挖掘 (TDM) 技术可用于处理 ISD 传感器收集的大量数据，因为这些数据是特定于时间、特定于年龄和特定于发展阶段的。本文通过分析 ISD Sensor Network Test Bed 近年来在 TDM 算法的辅助下收集的基线数据，计算出事件流中的频率事件。结果证实，TDM 技术是验证 ISD 性能的有效工具，在数据流中发现的频繁事件不仅显示了传感器响应的每日周期，还建立了不同类型传感器的响应序列，并通过实际实验装置进行了验证。一些异常模式可能具有预测系统故障的潜力。