This work applies a forecasting-based anomaly detection approach by using an optimized multi-output Temporal Fusion Transformer (TFT) model and multivariate time-series digital flight data acquired from a diverse set of on-board sensors. Nominal flight behavior forecasting capability is achieved through training a TFT model using nominal flight data only. This TFT model is then used in a forecasting-based anomaly detection setting to detect anomalous events relating to unstable approach (UA). Investigations that were conducted to support this approach included hyperparameter optimization, Root Mean Squared Error (RMSE) threshold profiling, and performance benchmarking. Flight Operational Quality Assurance (FOQA) digital flight data was used to conduct the analyses.
Forecasting-Based Anomaly Detection with Temporal Fusion Transformers Using Flight Operational Quality Assurance Data
29.09.2024
847795 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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