  Spacecraft faces various situations when carrying out exploration missions in
complex space, thus monitoring the anomaly status of spacecraft is crucial to
the development of \textcolor{blue}{the} aerospace industry. The time series
telemetry data generated by on-orbit spacecraft \textcolor{blue}{contains}
important information about the status of spacecraft. However, traditional
domain knowledge-based spacecraft anomaly detection methods are not effective
due to high dimensionality and complex correlation among variables. In this
work, we propose an anomaly detection framework for spacecraft multivariate
time-series data based on temporal convolution networks (TCNs). First, we
employ dynamic graph attention to model the complex correlation among variables
and time series. Second, temporal convolution networks with parallel processing
ability are used to extract multidimensional \textcolor{blue}{features} for
\textcolor{blue}{the} downstream prediction task. Finally, many potential
anomalies are detected by the best threshold. Experiments on real NASA SMAP/MSL
spacecraft datasets show the superiority of our proposed model with respect to
state-of-the-art methods.
