Anomaly Detection Method for Spacecrafts Based on Association Rule Mining
Anomaly Detection Method for Spacecrafts Based on Association Rule Mining
复制标题
基于关联规则挖掘的航天器异常检测方法
DOI：
10.11230/jsts.17.1_1
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
S. Nakasuka
中科院分区：
文献类型：
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作者：
T. Yairi;N. Ishihama;Yoshikiyo Kato;K. Hori;S. Nakasuka
提出了一种基于数据挖掘技术的航天器系统异常检测方法。该方法通过对学习阶段获得的时间序列数据进行模式聚类和关联规则挖掘，以规则集的形式自动构建系统行为模型，然后利用获取的规则对后续的在线数据进行检查，从而检测异常。这种方法的一个主要优点是，它需要很少的先验知识的系统。
This paper proposes a novel anomaly detection method for spacecraft systems based on data—mining techniques. This method automatically constructs a system behavior model in the form of a set of rules by applying pattern clustering and association rule mining to the time—series data obtained in the learning phase, then detects anomalies by checking the subsequent on—line data with the acquired rules. A major advantage of this approach is that it requires little a priori knowledge on the system.