An unsupervised root cause analysis method for satellite on-orbit anomalies based on causal discovery

Abstract

For a modern satellite, there are usually thousands of telemetry parameters. How to find the root cause of the satellite anomalies using telemetry data is an important but difficult task. This paper proposes an unsupervised root cause analysis method for satellite on-orbit anomalies based on causal discovery. The method includes a component impact analysis method based on multivariate-to-one transfer entropy, which can quickly identify components that cause system anomalies; and a pruning strategy based on the change of causality are proposed to screen and identify the parameters of the components to determine the root cause of the anomalies. Simulation experiments and real case verification show that the accuracy, recall, and F1-score of the method in this paper all reach more than 80%, which is greatly improved compared with the existing methods. The case study also shows that when anomalies occur, the root cause analysis method can detect the root cause using only 4 min of anomalous information, which helps to stop the spread of anomalies more effectively.