Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
Source abstract
Abstract
Flight Operations Quality Assurance (FOQA) is an internationally recognized solution to ensure the safety of civil aircraft flights based on Quick Access Recorder (QAR) data. The traditional approach to anomaly detection in civil aviation is to detect the over-limit values of monitoring parameters for each monitoring event based on the standards issued by civil aviation authorities. Usually, for each anomaly detection operation routine, this only works for one monitoring event. Furthermore, the causal analyses for the detected anomaly events are based on the relevant worker’s expertise. In order to improve the efficiency of FOQA, this paper proposes an automated anomaly detection and causal analysis method called MAD-XFP. Due to the unique industry characteristics of QAR data and the requirements of FOQA, feature engineering and hyper-parameter optimization techniques are utilized to enhance the machine learning model. The proposed method can monitor multiple events in one routine and provide a causal analysis. In the causal analysis process, the Shapley additive interpretation method is applied to produce analysis report for detected anomalies. Experimental evaluations are conducted on real civil aviation datasets. The experimental results show that the proposed method can efficiently and automatically detect different abnormal events with high precision in the approach phase and produce preliminary causal analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Risk and Safety Analysis · Anomaly Detection Techniques and Applications · Occupational Health and Safety Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Technology, Tiangong University, Tianjin 300387, China
Funding
Research Fund of the Tianjin Natural Science Foundation
References (21)
Show source-provided references
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