Satellite Unsupervised Anomaly Detection Based on Deconvolution-Reconstructed Temporal Convolutional Autoencoder

Anomaly detection for orbiting satellites has become a paramount research focus in the aerospace domain. Data-driven methodologies, employing high-dimensional telemetry data, have exhibited significant potential for timely anomaly detection without the need for pre-existing models or rules. In this paper, we introduce a novel satellite anomaly detection method, the Deconvolution-reconstructed Temporal Convolutional Autoencoder (DRTCAE), which utilizes preprocessed telemetry data. The DRTCAE consists of a convolutional encoder and a deconvolutional decoder, each leveraging the parallelism of stacked Advanced Dilated Causal Convolutional (ADCC) blocks. This innovative architecture facilitates efficient multi-scale nonlinear transformations, which are crucial for extracting time-dependent features from various telemetry data sources and enabling accurate anomaly detection. Through extensive experiments with telemetry data from the Zhuhai-1 OVS3A satellite and an unspecified GEO satellite, our findings demonstrate that the DRTCAE-based approach outperforms traditional multivariate regression techniques. In conclusion, our results underscore the DRTCAE as an exceptionally proficient method for detecting anomalies in orbiting satellites, thereby contributing to the improvement of overall reliability and safety in space missions.