(International Council of the Aeronautical Sciences (ICAS), 2021-09)
Emara, Mariam; dos Santos, Marcos; Chartier, Noah; Ackley, Jamey; Puranik, Tejas G.; Payan, Alexia P.; Kirby, Michelle R.; Pinon, Olivia J.; Mavris, Dimitri N.
The hazards posed by turbulence remain an important issue in commercial aviation safety analysis. Turbulence is among the leading cause of in-flight injury to passengers and flight attendants. Current methods of turbulence detection may suffer from sparse or inaccurate forecast data sets, low spatial and temporal resolution , and lack of in-situ reports. The increased availability of flight data records offers an opportunity to improve the state-of-the-art in turbulence detection. The Eddy Dissipation Rate (EDR) is consistently recognized as a reliable measure of turbulence and is widely used in the aviation industry. In this paper, both classification and regression supervised machine learning models are used in conjunction with flight operations quality assurance (FOQA) data collected from 6,000 routine flights to estimate the EDR (and thereby turbulence severity) in future time horizons. Data from routine airline operations that encountered different levels of turbulence is collected and analyzed for this purpose. Results indicate that the models are able to perform reasonably well in predicting the EDR and turbulence severity around 10 seconds prior to encountering a turbulence event. Continuous deployment of the model enables obtaining a near-continuous prediction of possible future turbulence events and builds the capability towards an early warning system for pilots and flight attendants.
(Georgia Institute of Technology, 2020-01)
Mangortey, Eugene; Monteiro, Dylan J.; Ackley, Jamey; Gao, Zhenyu; Puranik, Tejas G.; Kirby, Michelle; Pinon, Olivia J.; Mavris, Dimitri N.
In recent years, the use of data mining and machine learning techniques for safety analysis,
incident and accident investigation, and fault detection has gained traction among the aviation
community. Flight data collected from recording devices contains a large number of heterogeneous
parameters, sometimes reaching up to thousands on modern commercial aircraft. More
data is being collected continuously which adds to the ever-increasing pool of data available for
safety analysis. However, among the data collected, not all parameters are important from a
risk and safety analysis perspective. Similarly, in order to be useful for modern analysis techniques
such as machine learning, using thousands of parameters collected at a high frequency
might not be computationally tractable. As such, an intelligent and repeatable methodology to
select a reduced set of significant parameters is required to allow safety analysts to focus on the
right parameters for risk identification. In this paper, a step-by-step methodology is proposed
to down-select a reduced set of parameters that can be used for safety analysis. First, correlation
analysis is conducted to remove highly correlated, duplicate, or redundant parameters
from the data set. Second, a pre-processing step removes metadata and empty parameters.
This step also considers requirements imposed by regulatory bodies such as the Federal Aviation
Administration and subject matter experts to further trim the list of parameters. Third,
a clustering algorithm is used to group similar flights and identify abnormal operations and
anomalies. A retrospective analysis is conducted on the clusters to identify their characteristics
and impact on flight safety. Finally, analysis of variance techniques are used to identify which
parameters were significant in the formation of the clusters. Visualization dashboards were
created to analyze the cluster characteristics and parameter significance. This methodology is
employed on data from the approach phase of a representative single-aisle aircraft to demonstrate
its application and robustness across heterogeneous data sets. It is envisioned that this
methodology can be further extended to other phases of flight and aircraft.