A methodology for quantitative data-driven safety assessment for general aviation

General aviation;Safety;Data-driven;Anomaly detection

Puranik, Tejas Girish ; Mavris, Dimitri Aerospace Engineering Briceno, Simon Schrage, Daniel Marais, Karen Eghbali, Hossein ; Mavris, Dimitri

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: General aviation;    Safety;    Data-driven;    Anomaly detection;   

Others  :  https://smartech.gatech.edu/bitstream/1853/59905/1/PURANIK-DISSERTATION-2018.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The safety record of aviation operations has been steadily improving for the past few decades, however, accident rates in General Aviation (GA) have not improved significantly compared toscheduled commercial airline operations. Various data-driven safety programs such as Flight Data Monitoring (FDM) that exist in commercial aviationdomain have percolated in GA with the aim ofimproving safety. While these programs have been shown to be effective in reducing accident rates, there are certain obstacles in their widespread implementation in the GA domain. The variability in recorded parameters in GA flight data recorders (FDR), heterogeneity in GA fleet, differentmissions flown, etc. are some of the important hurdles. Additionally, existing techniques ofanalysis such as exceedance detection are designed to identify known unsafe conditions butare potentially blind to safety-critical conditionsthat may be captured in flight data records but arenot present in the set of predefined safety events. The overarching objective of this dissertation is to develop a methodology that can provide objective metrics for quantifying GA flight safety, enable automatic identification of anomalous operations, and provide predictive capabilities that will complement existing approaches. The dissertation presents the use of energy-based metrics as objective currency that can be used for quantifying flight safety across the heterogeneous GA fleet. An anomaly detection framework is developed using the defined safety metrics for identifying different types of anomalies (flight-level andinstantaneous) in GA operations. And finally, a novel technique of calibrating aircraft performance models used in safety analysis starting from a generic GA model is developed. Different options for calibration depending on the type of calibration data available are proposed and tested to be applicable in multiple scenarios. The developed methodology can be used for retrospective analysis as well as flight training for improving safety.