基于时空自注意力的航天器电源系统故障诊断

航天器电源系统是航天器关键子系统之一，其运行状态直接影响整个航天器系统的寿命和性能，因此采用先进的技术对电源系统进行故障诊断，以此提高航天器在轨可靠性和安全性成为目前故障诊断领域的研究重点。基于深度学习的方法具有拟合能力强、特征提取丰富等优势，成为故障诊断领域的主流方法。然而，在航天器电源系统故障诊断领域，主流的故障诊断方法无法捕获序列的长期依赖关系且局限于时间维度建模，严重影响故障诊断方法的性能。因此，本文提出一种基于时空自注意力机制的方法，对航天器电源系统进行高效准确的故障诊断。方法采用基于Transformer的编码器结构提取空间航天器遥测数据中的高维特征，并对其中的自注意力机制进行优化，采用时间卷积提取处理时序特征信息，并采用时间、空间双向自注意力机制提取数据中的时空特征，然后对模型提取的特征进行映射得到空间航天器故障诊断结果。最后在航空电源系统数据集上开展相关实验。实验结果表明与目前故障诊断领域常用的方法进行相比，所提方法具有更强的故障表征提取能力，可有效提高航天器电源系统故障诊断能力。

Spacecraft power system fault diagnosis based on spatio-temporal self-attention

The spacecraft power system is one of the key subsystems, and its operational status directly affects the lifespan and performance of the entire spacecraft system. Therefore, employing advanced technology for fault diagnosis of the power system to improve the reliability and safety of spacecraft in orbit has become a research focus in the field of fault diagnosis. Methods based on deep learning have advantages such as strong fitting ability and rich feature extraction, making them the mainstream approach in the field of fault diagnosis. However, in the field of fault diagnosis of spacecraft power systems, mainstream fault diagnosis methods cannot capture the long-term dependency of sequences and are limited to modeling in the time dimension, severely affecting the performance of fault diagnosis methods. Therefore, this paper proposes a method based on spatio-temporal self-attention mechanism for efficient and accurate fault diagnosis of spacecraft power system. The method adopts a Transformer-based encoder structure to extract high-dimensional features from spacecraft telemetry data, optimizes the self-attention mechanism therein, uses temporal convolution to extract temporal feature information, and employs both temporal and spatio bidirectional self-attention mechanisms to extract spatio-temporal features from the data. Finally, the features extracted by the model are mapped to obtain the fault diagnosis results of spacecraft power system. Relevant experiments are conducted on the spacecraft power system dataset. The experimental results show that compared with commonly used methods in the field of fault diagnosis, the proposed method has stronger fault representation extraction ability, which can effectively improve the fault diagnosis capability of spacecraft power systems.