Knowledge discovery or data mining techniques are widely used for anomaly detection in the commercial aviation domain to retrospectively improve operational safety. However, in the general aviation domain, especially for rotorcraft, analyses of flight data records for anomaly detection are not as prevalent. In this study, ADS-B data from a helicopter tour operator will be used to develop a prototype framework for uncovering patterns from routine flights. The ADS-B data contains two types of information: 1) time series of various flight parameters and 2) trajectory parameters. Various knowledge discovery techniques able to handle the aforementioned data types are explored and a few promising methods are applied to the ADS- B data of a helicopter tour operator in Hawaii. From the clustering results, patterns in the flight data records can be observed and can then be used by Subject-Matter Experts (SMEs) to facilitate the detection of anomalies. With this framework in place, rotorcraft operators will be able to analyze their routine flight data to not only monitor the safety of their operations but also to acquire knowledge on their operational patterns.
Hsiang-Jui Chin, Alexia Payan, Charles Johnson, and Dimitri N. Mavris, “Knowledge Discovery Within ADS-B Data from Routine Helicopter Tour Operations", AIAA Aviation 2020 Forum, (AIAA 2020-2872), Virtual, 15-19 Jun 2020, https://doi.org/10.2514/6.2020-2872