A Matching-Based Method for Anomaly Verification in Spacecraft Telemetry

Automatic anomaly detection for time series is essential for supervising the system operation and diagnosing abnormal events in the spacecraft system. Many anomaly detection approaches have been proposed in recent years. However, most anomaly detectors give scores to the detected anomalies solely according to the information collected during the learning and detection processes, which may not be dependable. As a result, when a large number of false alarms are detected, it is difficult to prune them using the given anomaly scores without sacrificing correctly detected true anomalies. In this paper, we propose a post-detection verification method based on a fast and accurate time series subsequence matching algorithm. Given a detected anomaly, we find its top-k most similar subsequences from the normal dataset (sequences assumed to be anomaly-free). Then a distance score is calculated for the detected anomaly. Also, P subsequences with the same length as the detected anomaly are extracted from the normal part (the part with no detected anomalies) of the test sequence. The distance scores of these P subsequences with respect to their closest counterparts in the normal dataset are calculated. Finally, we compare the distance score of the detected anomaly with the distance scores of the P normal subsequences to verify or reject it as a true anomaly. To evaluate the proposed method, we have created a challenging dataset MRO-SIN by injecting anomalies into the Mars Re-connaissance Orbiter (MRO) dataset, to allow for quantitative assessment. A stacked-predictor-based anomaly detector generates many false alarms and an Fl score of 0.512 on the MRO-SIN dataset. The new anomaly verification method significantly reduces the number of false alarms and improves the Fl score from 0.512 to 0.676.