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Detecting anomalous behaviors in multivariate systems using machine learning

Farcy, Benjamin and Gaurier, Alric Detecting anomalous behaviors in multivariate systems using machine learning. (2020) In: 1st International Conference on Cognitive Aircraft Systems - ICCAS 2020, 18 March 2020 - 19 March 2020 (Toulouse, France). (Unpublished)

(Document in English)

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Aircrafts are complex systems that are generating more and more data. An Airbus A320 equipped with FOMAX (Flight Operations and MAintenance eXchanger) records 24000 parameters and a Pratt & Whitney PW1000G GTF engine incorporates 5000 sensors, leading to terabytes of data being recorded for each flight. Modernization of military aircrafts and usage of Unmanned Aerial Vehicle (UAV) fleets also lead to large quantity of data. Detecting anomalies in those systems is valuable as a warning system to detect unexpected behaviors, faulty systems to be replaced or even discard readings of faulty sensors.

Item Type:Invited Conference
Audience (conference):International conference without published proceedings
Uncontrolled Keywords:
Institution:Other partners > Capgemini (FRANCE)
Deposited On:09 May 2021 15:17

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