Anomaly Detection Method of Aircraft System using Multivariate Time Series Clustering and Classification Techniques

Abstract

The paper presents an anomaly detection method that identifies and explains anomalies in an aircraft system based on explainable multivariate time series clustering techniques. The method considers the cyclicity of each variable within the flight phases and selects those behind the anomalies. It combines the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and a modified Dynamic Time Warping (DTW) distance algorithms to detect abnormal behavioral profiles within the collected flight phases without any prior knowledge on the system's behavior. The proposed method explains those abnormal profiles compared to normal profiles using a new importance score. Profiles are detected using the Time Series Forest (TSF) and the silhouette criterion. The method is trained and tested using a sample from the Bombardier's Aircraft Health Monitoring System. It distinguishes the normal and abnormal behaviors by achieving a clustering silhouette score of 0.95 and detects unknown profiles with a precision of 89%.