Abstract: Anomaly detection of spacecraft telemetry data is a key technology to identify the status of spacecraft and ensure the safe and reliable operation of spacecraft. However, anomaly detection of spacecraft telemetry data usually faces problems such as large dimensionality of time series data, unbalanced anomalies, and lack of labeled samples. In response to these problems, a deep anomaly detection model is proposed based on the idea of anomaly detection. Specifically, according to the strong temporal correlation of telemetry data, a long short term memory network with an attention mechanism is used to establish a telemetry data prediction model. At the same time, in order to overcome the problem of few abnormal labels and high data dimensions of spacecraft telemetry data, a fine tuning transfer learning method is used to optimize the prediction model, and a fully connected layer is used to unify the dimensions of different data sets, by which the accuracy of the transfer learning model and the capacity for anomaly detection are improved. Two spacecraft data sets released by NASA are taken as the experimental object, and the proposed anomaly detection method is used to identify the abnormal state of the data set. The results show that compared with the classic anomaly detection algorithm, the introduction of transfer learning can significantly improve the performance of the model. The experimental results are better than the current common anomaly detection models, which proves the effectiveness of the method.

Key words: anomaly detection, transfer learning, long short term memory, spacecraft telemetry data