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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Bibliografische Detailangaben

Hauptverfasser:	Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2025-02-01
Schriftenreihe:	Aerospace
Schlagworte:	flight data monitoring anomaly detection unsupervised machine learning flight safety
Online-Zugang:	https://www.mdpi.com/2226-4310/12/2/151
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