Title:
Application of Machine Learning Techniques to Parameter Selection for Flight Risk Identification
Application of Machine Learning Techniques to Parameter Selection for Flight Risk Identification
Author(s)
Mangortey, Eugene
Monteiro, Dylan J.
Ackley, Jamey
Gao, Zhenyu
Puranik, Tejas G.
Kirby, Michelle
Pinon, Olivia J.
Mavris, Dimitri N.
Monteiro, Dylan J.
Ackley, Jamey
Gao, Zhenyu
Puranik, Tejas G.
Kirby, Michelle
Pinon, Olivia J.
Mavris, Dimitri N.
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Abstract
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.
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Date Issued
2020-01
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