A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data

A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data

This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). The paper applies Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Network (CNN), classic Transformer architecture, and LSTM comb...

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Bibliographic Details
Main Authors: Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Aerospace
Subjects:
flight data monitoring
anomaly detection
unsupervised deep learning
flight safety
Online Access:https://www.mdpi.com/2226-4310/12/7/645
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