Prediction Method of Spacecraft Health Status Based on Unsupervised Clustering and LSTM Networks Learning

Health state prediction is a key technology to ensure the safe and stable operation of spacecraft in orbit from a system level. This paper proposes a method for predicting the health status of spacecraft based on unsupervised clustering and long short term memory (LSTM) networks, in response to the characteristic of performance degradation in key components of mechatronics. This method first extracts high dimensional time domain features of multi dimensional parameters of a single component of spacecraft, and fuses them into health factors that reflect the operational status of components through PCA method. Then, it combines unsupervised clustering algorithm to identify different evolution stages of faults. Finally, LSTM network is used to construct a health state evolution prediction model for each degradation stage, achieving health state prediction of spacecraft component. This article takes the key component of the control system, the Control Moment Gyroscope (CMG), as an example to experimentally verify the effectiveness of the above algorithm.