Title:
Knowledge Discovery Within ADS-B Data from Routine Helicopter Tour Operations

dc.contributor.author Chin, Hsiang-Jui
dc.contributor.author Payan, Alexia P.
dc.contributor.author Mavris, Dimitri N.
dc.contributor.author Johnson, Charles C.
dc.contributor.corporatename Georgia Institute of Technology. Aerospace Systems Design Laboratory en_US
dc.contributor.corporatename Federal Aviation Administration en_US
dc.contributor.corporatename American Institute of Aeronautics and Astronautics
dc.contributor.corporatename Georgia Institute of Technology. Aerospace Systems Design Laboratory
dc.date.accessioned 2020-07-02T20:31:41Z
dc.date.available 2020-07-02T20:31:41Z
dc.date.issued 2020-06
dc.description Presented at AIAA Aviation 2020 Forum en_US
dc.description.abstract Knowledge discovery or data mining techniques are widely used for anomaly detection in the commercial aviation domain to retrospectively improve operational safety. However, in the general aviation domain, especially for rotorcraft, analyses of flight data records for anomaly detection are not as prevalent. In this study, ADS-B data from a helicopter tour operator will be used to develop a prototype framework for uncovering patterns from routine flights. The ADS-B data contains two types of information: 1) time series of various flight parameters and 2) trajectory parameters. Various knowledge discovery techniques able to handle the aforementioned data types are explored and a few promising methods are applied to the ADS-B data of a helicopter tour operator in Hawaii. From the clustering results, patterns in the flight data records can be observed and can then be used by Subject-Matter Experts (SMEs) to facilitate the detection of anomalies. With this framework in place, rotorcraft operators will be able to analyze their routine flight data to not only monitor the safety of their operations but also to acquire knowledge on their operational patterns. en_US
dc.description.sponsorship FAA Pegasus Project 2 en_US
dc.identifier.citation Chin, H.J., Payan, A.P., Mavris, D. and Johnson, C., 2020. Knowledge Discovery within ADS-B Data from Routine Helicopter Tour Operations. In AIAA AVIATION 2020 FORUM (p. 2872). DOI: 10.2514/6.2020-2872 en_US
dc.identifier.doi https://doi.org/10.2514/6.2020-2872 en_US
dc.identifier.uri http://hdl.handle.net/1853/62991
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher Georgia Institute of Technology
dc.publisher.original American Institute of Aeronautics and Astronautics (AIAA)
dc.relation.ispartofseries ASDL; en_US
dc.subject Knowledge discovery en_US
dc.subject ADS-B data en_US
dc.subject Clustering analysis en_US
dc.title Knowledge Discovery Within ADS-B Data from Routine Helicopter Tour Operations en_US
dc.type Text
dc.type.genre Paper
dspace.entity.type Publication
local.contributor.author Payan, Alexia P.
local.contributor.author Mavris, Dimitri N.
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.contributor.corporatename Aerospace Systems Design Laboratory (ASDL)
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication a8736075-ffb0-4c28-aa40-2160181ead8c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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