Flight data anomaly detection and diagnosis with variable association change

ABSTRACTAircraft sensors generate multivariate time series during flights, where each sensor corresponds to one variable. During normal operation mode, the associations (dependencies) among variables are mainly stationary. One type of flight anomaly that is of interest relates to variable association change. Detection and diagnosis of such type of anomaly need to pinpoint the time series, i.e., variables related to association change, which helps in understanding the underlying mechanisms of anomalies. However, it is hard to detect such change because the variable associations are usually unknown and complicated, and the anomalous samples are usually insufficient for learning the substandard association. In this work, we present a neural network that can 1) detect this type of anomalies given multivariate time series as input; 2) locate the association change by learning the nonlinear variable associations from both normal data and the detected anomalies. Specifically, we leverage the learned model from normal data to learn the faulty association of the anomalies. Experiments using simulated and real-world flight data show that our model outperforms existing methods in flight anomaly detection and diagnosis.