Unsupervised Deep Learning for Fault Detection on Spacecraft Using Improved Variational Autoencoder
Gang Xiang, Ran Tao, Yu Peng, Kun Tian, Chen Qu
Abstract
Fault detection is important for improving the reliability of spacecraft, ensuring the long-term stable operation, and reducing the economic loss caused by failure. In order to solve the problems such as the large amount of test data, the scarcity of fault data samples and the real-time requirements in the field of spacecraft fault detection, an improved unsupervised deep learning algorithm based on Variational Autoencoder (VAE) is proposed. The algorithm adopts Gated Recurrent Unit (GRU) based recurrent neural networks as encoder to automatically extract features of input data, and then uses VAE to learn the correlation features of multiple test data. The proposed network, trained only on the normal training dataset, is a typical unsupervised method which could learn features and reconstruct the data on the training set with a small loss. Once the reconstruction loss of the input data is larger than the pre-set threshold, the corresponding input data is considered as fault data. Experiments show that the proposed method is feasible and can effectively detect faults.