Control Moment Gyroscope Anomaly Detection Based on Sparse Autoencoder

The Control Moment Gyroscope (CMG) is an important executing part of spacecraft, and its anomaly detection has a key influence on the reliability of spacecraft. With the rapid growth of the number of spacecraft and telemetry data, data-driven spacecraft anomaly detection methods have received extensive attention, and such methods may become an important means of spacecraft anomaly detection. However, at present, the spacecraft is faced with the problem of unknown anomalies and lack of abnormal data during orbit. To solve this problem, a sparse autoencoder (SAE) based CMG anomaly detection method was proposed, which takes the telemetry signal of CMG as the object and lacks the sample of anomalous telemetry signal in training data. Firstly, data sample selection and processing were carried out to normalize the data of each telemetry value. Secondly, the SAE model was constructed, and the model was trained with the training set data. Then the sample reconstruction error of the test set was calculated to complete the anomaly detection of the CMG. Finally, it was compared with the traditional artificial threshold monitoring method. The results indicated that the CMG anomaly detection method based on SAE can effectively detect the anomaly samples of the CMG telemetry data, and has better anomaly detection performance than the traditional artificial threshold monitoring method.