Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning

Anomaly detection based on telemetry data is a major issue in satellite health monitoring, given that it can identify unusual or unexpected events to avoid serious accidents and ensure the safety and reliability of operations. This study proposes a jointly optimal one-class support vector machine with dictionary learning (JODL-OC) framework for multivariate anomaly detection in telemetry. Dictionary learning (DL) extracts the correlation and local dynamics of time series into a sparse matrix, and the one-class support vector machine (OCSVM) detects anomalies in the transformed sparse space. The optimisation objective of DL is modified by introducing a novel regularisation term called soft-boundary OCSVM, so that the JODL-OC seamlessly integrates DL, sparse representation, and classifier training into a unified model to jointly learn the features and decision boundary. Furthermore, we propose an iterative approach to solve the optimisation function and obtain the optimal JODL-OC framework for anomaly detection. A case analysis based on satellite antenna telemetry data reveals that the proposed method improves the detection precision, F1-score, FPR and FAR compared to other existing anomaly detection methods, particularly to illustrate the superior performance in correlation anomalies.