Spacecraft Anomaly Detection

Spacecraft anomaly detection aims to automatically identify malfunctions in spacecraft systems using data analysis and computer vision techniques, improving mission safety and efficiency. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, and transformer architectures to analyze telemetry data and image feeds for anomaly identification and spacecraft component recognition. These methods are being refined to improve accuracy, robustness across diverse operational conditions, and real-time processing capabilities, particularly for autonomous on-orbit servicing and debris removal. The successful development of these techniques is crucial for enhancing the reliability and safety of space operations.