# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 1 Introduction
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Monitoring Items**} & **Monitoring Parameters** & \multirow{2}{*}{**Monitoring Point**} & \multicolumn{2}{c}{**Deviation Limit Value**} \\ \cline{3-5}  & & & **Mild Deviation** & **Severe Deviation** \\ \hline \multirow{5}{*}{Large Decent Rate} & & 610 m (2000 ft)\(\sim\)305 m (1000 ft) & \(>\)457 m/min & \(>\)549 m/min \\  & & 610 m (2000 ft)\(\sim\)305 m (1000 ft) & (1500 ft/min) & (1800 ft/min) \\  & & 305 m (1000 ft)\(\sim\)152 m (500 ft) & \(>\)396 m/min & \(>\)457 m/min \\  & & & (1300 ft/min) & (1500 ft/min) \\  & & 152 m (500 ft)\(\sim\)15 m (50 ft) & \(>\)335 m/min & \(>\)396 m/min \\  & & & (1100 ft/min) & (1300 ft/min) \\ \hline \multirow{5}{*}{Large Approach Roll Angle} & & 457 m (1500 ft)\(\sim\)152 m (500 ft) & \(>\)30\({}^{\circ}\) & \(>\)35\({}^{\circ}\) \\  & & 152 m (500 ft)\(\sim\)61 m (200 ft) & \(>\)15\({}^{\circ}\) & \(>\)20\({}^{\circ}\) \\ \cline{1-1}  & & 61 m (200 ft)\(\sim\)15 m (50 ft) & \(>\)8\({}^{\circ}\) & \(>\)10\({}^{\circ}\) \\ \hline High Approach Speed & IAS, VAPP & 152 m (500 ft)\(\sim\)15 m (50 ft) & \(>\)(VAPP + 15) kn & \(>\)(VAPP + 20) kn \\ Low Approach Speed & IAS, VAPP & 305 m (1000 ft)\(\sim\)15 m (50 ft) & \(<\)(VAPP \(-\) 5) kn & \(<\)(VAPP \(-\) 10) kn \\ ILS Glide Slope Deviation & GLIDE\_DEVC & below 305 m (1000 ft) & \(>\)1.0 point & \(>\)1.5 point \\ ILS Heading Deciation & LOC\_DEVC & below 305 m (1000 ft) & \(>\)1.0 point & \(>\)1.5 point \\ Low-Altitude High Speed & IAS & below 762 m (2500 ft) & \(>\)230 kn & \(>\)250 kn \\ \hline \hline \end{tabular}
\end{table} 
Table 1: FOQA monitoring items and requirements for Airbus A320 (Airbus, Leiden, The Netherlands) (in approach phase) [1]. To address the increasing demands of FOQA, leveraging artificial intelligence and machine learning technologies to improve the efficiency and quality of FOQA has become a priority for civil aviation. EXtreme Gradient Boosting(XGBoost) [4] is an ensemble machine learning algorithm based on decision trees, widely applied for regression and classification tasks. This model has notable advantages in handling anomalies, balancing data distribution, and providing interpretability [5,6]. However, there are still several challenges to apply it to QAR data in practice. Due to the inherent characteristics of QAR data, data pre-processing is essential and must be tailored to the specific requirements of the anomaly detection task. Additionally, the XGBoost model involves over 10 parameters, including general parameters, tree-specific parameters, learning task parameters, etc. Consequently, optimizing the model to adapt to anomaly detection in QAR data is also a complex task. To achieve intelligent and automated flight anomaly detection and causal analysis in civil aviation, we propose a comprehensive method, MAD-XFP. This method is based on XGBoost, incorporating automated feature engineering techniques and AutoML model optimization. The proposed method can effectively identify various abnormal events and composite events in one operation routine, facilitating automated causal analysis.