2011 IEEE/AIAA 30th Digital Avionics Systems Conference · 2011 · 37 citations · 0 references
ComputingAerospace EngineeringData ScienceData AnalyticsData MiningTime Series AnalysisAnomaly DetectionAnomaly Detection In Time SeriesCluster DetectionClustered Data AnalysisOutlier DetectionTime Series ClusteringCluster AnalysisCluster-based Anomaly DetectionCluster-based Outlier DetectionData Anomaly DetectionFlight OperationsHigh-dimensional Anomaly DetectionHigh-dimensional Data ClusteringTakeoff Conditions
▓ Developed a method to detect anomalous flights using cluster analysis • Advantages ▓ Minimum prior knowledge of data required S Identify multiple nominal data patterns • Limitation ▓ Current transformation method is only applicable to flight phases start or end with a specific event ▓ Applied the proposed method on a dataset of B777 flights for takeoff phase and approach phase ▓ Initial evaluation indicates that cluster analysis is a promising approach for anomaly detection in FDR/QAR data