Fault Prediction for Solar Array Based on Long Short-Term Memory and Autoencoder

Satellites are intricate systems that consist of large number of interconnected devices. Consequently, it generates tremendous amount of telemetry data during its lifetime. If the system information of solar array that contained in the telemetry data can be used to forecast the future state, then the malfunction alarm and in-orbit maintenance can be implemented. At present, research on solar arrays basically focuses on state monitoring and outlier detection, and almost no fault prediction is involved. In this paper, a data-driven faults prediction method for solar array based on long short-term memory (LSTM) networks and autoencoder (AE) is proposed. This novel fault prediction method works by creating a prediction model using the collected time series. The temporal features of sensor data are captured with LSTM networks, which perform well in extracting the features of time series. The predicted sequence is reconstructed by the identifying model, and the reconstruction error of the input sequence which is used as a health indicator to estimate the states of devices. The data used in verification experiment in this paper is the telemetry data of an orbiting satellite. The experiments show that the proposed method can detect the occurrence of failures several days in advance compared with the traditional method.