Comparative Analysis of LSTM Variants for Fault Detection and Classification in Aircraft Control Surfaces

Aircraft control surfaces play a critical role in ensuring safe and efficient flight. Faults in these surfaces could lead to catastrophic consequences. This paper investigates the application of Long Short-Term Memory (LSTM) networks for fault detection and classification in aircraft control surfaces. Four deep learning models: LSTM, Stacked-LSTM, Bi-LSTM, and Attention-based LSTM (ALSTM), were trained, validated, and tested to classify faults based on residual features. The methodology involved data generation, preprocessing, normalization, and training the models over 200 epochs. Evaluation metrics, including confusion matrices, precision, recall, and F1-scores, were used to assess model performance. Results show that Bi-LSTM achieved the highest accuracy (98.93%) and lowest loss (0.0264), significantly outperforming other models in fault detection, particularly for challenging fault types such as hard-over and lock-in-place. ALSTM followed closely, with notable performance improvements over standard and stacked LSTM models.