Anomaly detection of aircraft system using kernel-based learning algorithm

Hyunseong Lee, Guoyi Li, Ashwin Rai, Aditi Chattopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


A real-time health monitoring framework is developed in this work to detect in-flight operational anomalies in aircraft subsystems. Relevant features with similar eigenvectors that characterizes dynamic flight behavior is extracted and used to train flight behavior and detect operational anomalies by comparison with statistical safety bounds. Additionally, the monitoring framework is implemented for real-time application by adopting kernel functions for computational acceleration. The accuracy and efficiency of the proposed algorithm is demonstrated with several case studies of operational anomalies in the aircraft engine systems.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
StatePublished - Jan 1 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameAIAA Scitech 2019 Forum


ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Aerospace Engineering


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