Among operations in the General Aviation community, one of the most important objectives is to improve safety across all ﬂight regimes. Flight data monitoring or Flight Op-erations Quality Assurance programs have p ercolated in the General Aviation sector withthe aim of improving safety by analyzing and evaluating ﬂight data. Energy-based metricsprovide measurable indications of the energy state of the aircraft and can be viewed as anobjective currency to evaluate various safety-critical conditions. The use of data miningtechniques for safety analysis, incident examination, and fault detection is gaining tractionin the aviation community. In this paper, we have presented a generic methodology foridentifying anomalous ﬂight data records from General Aviation operations using energy-based metrics and clustering techniques. The sensitivity of this methodology to variouskey parameters is quantiﬁed using diﬀerent experiments. A demonstration of this metho d-ology on a set of actual ﬂight data records as well as simulated ﬂight data is presentedhighlighting its future potential.
Puranik, T., Jimenez, H., and Mavris, D.N., “Utilizing Energy Metrics and Clustering Techniques to Identify Anomalous General Aviation Operations,” AIAA Infotech @ Aerospace, American Institute of Aeronautics and Astronautics, Jan 2017, http://dx.doi.org/10.2514/6.2017-0789