# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 1 Introduction  
Flight Operations Quality Assurance (FOQA) [1] is an important aspect of airline safety management and has been widely recognized by the global civil aviation industry. By analyzing daily flight data collected by digital flight data recorders, these data are useful for detecting unsafe events and potential hazards during the flight process, optimizing air traffic control procedures, and improving flight quality. The Quick Access Recorder (QAR) is an important instance of digital equipment that captures a large amount of parameters during aircraft flights, including crew actions, aircraft status, engine performance, fuel consumption, and additional pertinent data. This recorded information [2] forms the essential data foundation (called QAR data) for FOQA. However, QAR data record a large number of flight parameters (over 2200 parameters for Airbus A320), and for each parameter, its collecting frequency, accuracy, and data format are not consistent. In addition, there are many data quality issues such as noise, anomalies, and missing data in collected QAR data, which create great challenges for downstream applications such as data mining, model training, abnormal flight detection, and casual analysis. QAR data need to be cleaned before further analysis is performed. On the other hand, the civil aviation industry in China has always been at the forefront of safety management. According to the 2022 China Civil Aviation Administration Statistical Bulletin, the ten-year rolling value of the major accident rate is 0.011 (per million flights), which is significantly lower than the average global industry. The vast majority of QAR data are within normal ranges. The quantity and proportion of data parameters related to certain anomaly events are relatively small, demonstrating an unbalanced data distribution. Overall, the unbalanced distribution and the low data quality, along with the unique industry characteristics of QAR data, present considerable challenges for FOQA. An aircraft usually goes through several stages to complete a flight, including taxi in and take off, climb, cruise, descent, approach, and landing. Different aircraft types usually have different classification standards. The approach phase refers to the process during which an aircraft systematically descends from its cruising altitude, transitioning towards to the destination airport. During the approach phase, the flight operation is susceptible to the influence of multiple factors, including adverse weather conditions, crew actions, and technical malfunctions. These influences may contribute to a high likelihood of over-limit events [3]. The approach phase is the most prone to anomalies throughout the entire flight process. So, this work focuses on anomaly detection in the approach phase. Traditional flight anomaly detection relies on the monitoring items and standards in the FOQA guidelines issued by the Civil Aviation Administration of China (CAAC) to identify over-limit parameter values. In this work, we focus mainly on the monitoring events involved in the approach phase, as shown in Table 1. We compare the values of monitoring parameters within the monitoring point range with the out-of-limit values to determine whether they belong to the categories of severe deviation, mild deviation, or normal data. The meaning of the monitoring parameters is shown in Table 2.  
However, the traditional working manner carries several inherent risks:  
1. It can only detect one anomaly in one operation routine. It lacks effectiveness in identifying composite abnormal events that involve multiple anomalies. 2. It does not support automated causal analysis of abnormal events. It relies heavily on expert experience to identify abnormal events and generate causal analysis reports.