Anomaly Detection in General-Aviation Operations Using Energy Metrics and Flight-Data Records

Among the 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-operations-quality-assurance programs have percolated in the general-aviation sector with the aim of improving safety by analyzing and evaluating flight data. Energy-based metrics provide measurable indications of the energy state of the aircraft, and can be viewed as an objective currency to evaluate various safety-critical conditions. The use of data-mining techniques for safety analysis, incident examination, and fault detection is gaining traction in the aviation community. In this paper, a generic methodology is presented for identifying anomalous flight-data records from general-aviation operations in the approach-and-landing phase. Energy-based metrics, identified in previous work, are used to generate feature vectors for each flight-data record. Density-based clustering and one-class classification are then used together for anomaly detection using energy-based metrics. A demonstration of this methodology on a set of actual flight-data records from routine operations, as well as simulated flight data, is presented, highlighting its potential for retrospective safety analysis. Anomaly detection using energy metrics, specifically, is a novel application presented here.