QAR Data-Driven Flight Anomaly Detection and Risk Warning: A Review of Statistical Learning and Machine Learning Methods in Aviation Safety

Abstract

Quick Access Recorder (QAR) data provide high-dimensional onboard flight records that are increasingly used for data-driven aviation safety analysis. As flight operations become more complex, conventional manual monitoring and threshold-based exceedance detection are often insufficient for identifying evolving risks in a timely and interpretable manner. This paper reviews QAR data-driven methods for flight anomaly detection and risk warning from the perspective of statistical learning and aviation data science. First, the main characteristics of QAR data are summarized, including multi-source heterogeneity, temporal dependence, missing values, noise, and severe class imbalance. Common preprocessing techniques, such as missing-value imputation, trajectory correction, feature engineering, downsampling, and imbalanced-data handling, are then reviewed. Second, existing methods are organized into four groups: statistical monitoring and rule-based methods, clustering and unsupervised anomaly detection, Bayesian and probabilistic risk reasoning, and hybrid machine learning with explainable AI. Representative approaches include statistical process control, association rules, Gaussian mixture models, CurveCluster, Fast-DTW, Bayesian networks, dynamic Bayesian networks, VAE-LSTM, MAD-XFP, and XGBoost with SHAP interpretation. The review further discusses typical applications in landing risk warning, takeoff risk assessment, flight operation pattern recognition, and aviation noise prediction. Finally, key challenges are summarized, including model interpretability, real-time deployment, cross-aircraft and cross-airport generalization, data quality, causal reasoning, and privacy-preserving collaboration. This review provides a structured reference for using QAR data to support aviation safety assessment, risk warning, and operational decision-making.