  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 data. The deep
learning models under investigation include Convolutional Neural Networks
(CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM)
networks, and Transformer-based architectures. Each of these models was trained
and validated using a comprehensive dataset sourced from multiple spacecraft
missions, encompassing diverse operational scenarios and anomaly types. Initial
results indicate that while CNNs excel in identifying spatial patterns and may
be effective for some classes of spacecraft data, LSTMs and RNNs show a marked
proficiency in capturing temporal anomalies seen in time-series spacecraft
telemetry. The Transformer-based architectures, given their ability to focus on
both local and global contexts, have showcased promising results, especially in
scenarios where anomalies are subtle and span over longer durations.
Additionally, considerations such as computational efficiency, ease of
deployment, and real-time processing capabilities were evaluated. While CNNs
and LSTMs demonstrated a balance between accuracy and computational demands,
Transformer architectures, though highly accurate, require significant
computational resources. In conclusion, the choice of deep learning
architecture for spacecraft anomaly detection is highly contingent on the
nature of the data, the type of anomalies, and operational constraints.
