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.

翻译：航天器运行具有极高关键性，要求具备无可挑剔的可靠性与安全性。确保航天器最佳性能需要早期检测并缓解异常状况，否则可能导致设备或任务失败。随着深度学习的出现，利用这些先进算法进行空间操作中的异常检测引发了广泛关注。本研究旨在比较不同深度学习架构在航天器数据异常检测中的有效性。研究的深度学习模型包括卷积神经网络、循环神经网络、长短期记忆网络和基于Transformer的架构。每个模型均使用来自多个航天器任务的综合数据集进行训练和验证，该数据集涵盖多样化的运行场景和异常类型。初步结果表明：CNN在识别空间模式方面表现出色，可能对某些类型的航天器数据有效；而LSTM和RNN在捕获航天器遥测时间序列中的时序异常方面具有显著优势。基于Transformer的架构因其能够同时关注局部与全局上下文，在异常隐蔽且持续时间较长的场景中展现出令人期待的性能。此外，研究还评估了计算效率、部署便利性与实时处理能力等考量因素。CNN和LSTM在准确性与计算需求间实现了平衡，而Transformer架构虽具有高准确性，但需要显著的计算资源。结论认为：航天器异常检测的深度学习架构选择高度取决于数据特性、异常类型及运行约束条件。