Imbalanced Aircraft Data Anomaly Detection

Abstract

Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task. First, long temporal data are difficult to extract contextual information with temporal correlation, and second, the anomalous data are rare in time series, causing normal/abnormal imbalance in anomaly detection, making the detector classification degenerate or even fail. To remedy the aforementioned problems, we propose a graphical temporal data analysis framework. It consists of three modules, named series-to-image (S2I), cluster-based resampling approach using Euclidean distance (CRD), and variance-based loss (VBL). Specifically, to better extract global information in temporal data from sensors, S2I converts the data to curve images to demonstrate abnormalities in data changes. CRD and VBL balance the classification to mitigate the unequal distribution of classes. CRD extracts minority samples with similar features to majority samples by clustering and oversamples them. And VBL fine-tunes the decision boundary by balancing the fitting degree of the network to each class. Ablation experiments on the Flights dataset indicate the effectiveness of CRD and VBL on precision and recall, respectively. Extensive experiments demonstrate the synergistic advantages of CRD and VBL on F1-score on Flights and three other temporal datasets.