A Comparative Study of Unsupervised Machine Learning Methods for Anomaly Detection in Flight Data: Case Studies from Real-World Flight Operations

This paper provides a comparative study of unsupervised machine learning (ML) methods for anomaly detection in flight data monitoring (FDM). The study applies various unsupervised ML techniques to real-world flight data and compares the results to the current state-of-the-art flight data analysis te...

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Autori principali:	Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2025-02-01
Serie:	Aerospace
Soggetti:	flight data monitoring anomaly detection unsupervised machine learning flight safety
Accesso online:	https://www.mdpi.com/2226-4310/12/2/151
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