Anomaly Detection from Distributed Flight Record Data for Aircraft Health Management

Detecting anomalous behavior from terabytes of flight record data has emerged as a crucial component for many systems for Aircraft Health Management. Very often, flight record data collected from various aircraft cannot be directly aggregated for anomaly analysis due to the proprietary nature of the data. This paper proposes a novel general framework for anomaly detection from distributed data sources that cannot be directly merged. In the proposed method, anomaly detection algorithm is first applied to data from individual aircraft and then their results are combined. We investigated eleven semi supervised anomaly detection algorithms, as well as four methods for combining anomaly detection results. Our experiments performed on simulated data have shown that particular anomaly detection algorithms and combining methods are more suitable for the task of distributed anomaly detection than others.