A methodology for quantitative data-driven safety assessment for general aviation
Puranik, Tejas Girish
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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 to scheduled commercial airline operations. Various data-driven safety programs such as Flight Data Monitoring (FDM) that exist in commercial aviation domain have percolated in GA with the aim of improving 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, different missions flown, etc. are some of the important hurdles. Additionally, existing techniques of analysis such as exceedance detection are designed to identify known unsafe conditions but are potentially blind to safety-critical conditions that may be captured in flight data records but are not 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 and instantaneous) 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.