Fault Detection by Mining Association Rules from Housekeeping Data

@inproceedings{Yairi2001FaultDB,
  title={Fault Detection by Mining Association Rules from Housekeeping Data},
  author={Takehisa Yairi and Yoshikiyo Kato and Koichi Hori},
  year={2001}
}
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. 
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