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An Application of DBSCAN Clustering for Flight Anomaly Detection During the Approach Phase

AIAA 2020-1851
Session: Learning Reasoning and Data Driven Systems II
Published Online:https://doi.org/10.2514/6.2020-1851
Abstract:

Safety is of paramount importance in aviation due to the catastrophic consequences of accidents. Consequently, efforts have been made over the years to research and improve flight safety analysis techniques. Recent improvements in data storage and processing capabilities enable new techniques in flight safety analysis. The predominant safety analysis technique used by airlines is event exceedance analysis, which combines Flight Operations Quality Assurance (FOQA) data and predefined parameter safety thresholds to identify risks to safety. However, there is a need to continuously enhance risk identification and improve flight safety analysis. There are several areas where event exceedance analysis is limited. Event exceedance analysis is constrained by a reliance on pre-determined safety thresholds, and the analysis of parameters at a single point in time with no consideration for the behavior of the aircraft prior to that point. Furthermore, the pre-defined safety thresholds often are not consistent across airports and airframes, and analysis is thus limited within airport or airframe type. This research addresses these gaps by developing and implementing a robust methodology for identifying anomalous flights, and providing further insight into risk and safety trends in the approach phase of flight. This was achieved by 1) leveraging a Hierarchical clustering algorithm to categorize airports into clusters, 2) detecting anomalous flights by leveraging the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and varying the length of the approach phase across the National Airspace System (NAS) as well as on an airport cluster level, 3) combining clustering results with an anomaly scoring algorithm to compare the detection of anomalous flights with current exceedance analysis event definitions and finally 4) investigating the impact of the length of the approach phase on outlier identification. Flight cluster labels were obtained through DBSCAN and flight anomaly scores were obtained through an anomaly scoring algorithm. Results from these studies showed that event flights (designated by event exceedance analysis) on average had higher anomaly scores than non-event flights. Airport clustering was shown to have a non-negligible impact on flight anomaly score, and should be investigated further. Finally, identifying anomalies by varying the length of the approach phase from 10 to 600 seconds prior to touchdown showed significant variability in the flight outlier designation. The last 100 seconds of approach are shown to be particularly volatile. By evaluating risk at single point, it is possible that event exceedance analysis is missing potentially dangerous events occurring during final approach. Applying a method that utilizes time series data shows promise in improving anomaly detection capabilities.