Anomaly detection in onboard-recorded flight data using cluster analysis

Abstract

A method has been developed to support Flight Operations Quality Assurance (FOQA) by identifying anomalous flights based on onboard-recorded flight data using cluster analysis techniques. Unlike current techniques, the method does not require pre-defined thresholds of particular parameters, but detects data patterns which differ from the majority of flights by considering all the available flight parameters. The method converts time series data from multiple flight parameters into a high dimensional data vector. Each vector captures all the available information for a single flight. Cluster analysis of the vectors is performed to identify nominal flights which are associated with large clusters and anomalous flights that do not belong to a specific cluster. The method was applied to a representative Digital Flight Fata Recorder (DFDR) dataset from an international airline. Detailed analysis was performed on takeoff and approach for 365 B777 flights. Abnormal flights were detected using the cluster technique which was able to identify anomalous behaviors including: high and low energy states, unusual pitch excursions, abnormal flap settings, high wind conditions. In addition, data clusters representing nominal conditions were also detected. Three distinct takeoff clusters were identified in the B777 data: one represented a majority of the takeoff cases, one correlated with a specific high altitude airport, one correlated with reduced power takeoffs. This initial evaluation indicates that cluster analysis is a promising approach for the identification of anomalous flights from onboard-recorded flight data.