CAFUC2

This dataset is dedicated to anomaly detection in flight training scenarios, constructed based on 120 original normal flight training CSV files with a total of 993,655 valid normal data records. To support the training, validation, and performance evaluation of anomaly detection models, four types of typical flight anomalies\u2014throttle surge, course deviation, engine failure, and excessive pitch\u2014have been injected into the normal data. The anomaly injection ratio is controlled within the range of 2% to 5%, which aligns with the frequency of common abnormal events in actual flight training.The dataset retains core flight training feature fields with a unified CSV format, free from missing values or invalid records. It can be directly applied to supervised, semi-supervised, or unsupervised anomaly detection algorithms such as Isolation Forest, Autoencoder, and LSTM. Additionally, this dataset provides high-quality, real-scenario data support for the development, optimization, and validation of flight training anomaly identification systems, facilitating the improvement of flight safety monitoring capabilities in aviation training.