Anomaly Detection of Control Moment Gyroscope Based on Working Condition Classification and Transfer Learning

Abstract

The process of human exploration of the universe has accelerated, and aerospace technology has developed rapidly. The health management and prognosis guarantee of spacecraft systems has become an important basic technology. However, with thousands of telemetry data channels and massive data scales, spacecraft systems are increasingly complex. The anomaly detection that relied on simple threshold judgment and expert manual annotation in the past is no longer applicable. In addition, the particularity of the anomaly detection task leads to the lack of fault data for training. Therefore, a data-driven deep transfer learning-based approach is needed for rapid analysis and accurate detection of large-scale data. The control moment gyroscope (CMG) is a significant inertial actuator in the process of large-scale, long-life spacecraft in-orbit operation and mission execution. Its anomaly detection plays a major role in the prevention and elimination of early failures. Based on the research of SincNet and Long Short-Term Memory (LSTM) networks, this paper proposed a Sinc-LSTM neural network based on transfer learning and working condition classification for CMG anomaly detection. First, a two-stage pre-training method is proposed to alleviate the data imbalance, using the Mars Reconnaissance Orbiter (MRO) dataset and a satellite dataset from NASA. Second, the Sinc-LSTM network is designed to enhance the local fitting and long-period memory ability of the model for CMG time series data. Finally, a dynamic threshold judgment anomaly detection method based on working condition classification is designed to accommodate threshold changes for CMG full-cycle anomaly detection. The method is validated on the spacecraft CMG dataset.

Keywords

• control moment gyroscope
• abnormal detection
• LSTM
• transfer learning