A Comparison of Deep Learning Architectures for Spacecraft Anomaly Detection

arXiv:2403.12864v1 Announce Type: new
Abstract: Spacecraft operations are highly critical, demanding impeccable reliability and safety. Ensuring the optimal performance of a spacecraft requires the early detection and mitigation of anomalies, which could otherwise result in unit or mission failures. With the advent of deep learning, a surge of interest has been seen in leveraging these sophisticated algorithms for anomaly detection in space operations. This study aims to compare the efficacy of various deep learning architectures in detecting anomalies in spacecraft …

abstract anomaly anomaly detection architectures arxiv comparison cs.lg deep learning detection mission operations performance reliability safety spacecraft type