关键词: 卫星遥测数据, 迁移学习, 深度学习, 异常检测

Abstract: In order to solve the problems of imbalanced data and lack of labeled samples faced by satellite telemetry data anomaly detection, a method of anomaly detection based on one dimensional convolutional neural network (1dCNN) transfer learning is proposed. Firstly, the source domain satellite data are used to pre train the 1dCNN to make the model’s convolutional layer have satellite state feature extraction capabilities; then the pre trained model is transferred to the target domain satellite lacking labeled data; finally, the labeled samples of the target domain are used to fine tune the pre trained model so as to achieve anomaly detection of the samples of the target domain test set. In order to make the 1dCNN adapt to the imbalance of satellite telemetry data samples, a cost sensitive training strategy is introduced. By establishing a dynamic loss function, the cs 1dCNN can improve the capacity of recognizing the abnormal samples. Experiments with the power subsystem data of two anonymous satellites verify the effectiveness and robustness of the proposed anomaly detection transfer method.

Key words: Satellite telemetry data, Transfer learning, Deep learning, Anomaly detection