Aviation Data Anomaly Detection Based on Graph Neural Networks
Flight Operational Quality Assurance(FOQA)data records detailed parameters of flight status,which is crucial for evaluating the quality and safety of flight operations.Traditional"Exceedance Detection"algorithm identifies abnormal behavior by comparing it with predefined thresholds.In contrast,Deep Learning methods can comprehensively and flexibly analyze FOQA data,improving the accuracy of abnormal behavior detection.The TAGDNet proposed in the paper is an innovative framework for multi-class abnormal detection in FOQA data,including key components such as Temporal Convolutional Networks,Graph Neural Networks,and Hierarchical Graph Pooling.The framework extracts temporal features through Temporal Convolutional Networks firstly,then propagates information between nodes through introducing Graph Neural Networks and finally obtains abnormal detection results through Hierarchical Graph Pooling.Through extensive experiments on publicly available FOQA multi-class abnormal detection datasets,it has been demonstrated that this method outperforms other state-of-the-art methods.
国家科技期刊平台
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