Fault diagnosis of spacecraft tracking telemetry and control systems based on residual network

DOI:	10.11809/bqzbgcxb2023.03.036
Keywords:	spacecraft; fault diagnosis; deep learning; PCA; ResNet
Abstract:	Due to the complex space environment, spacecraft telemetry signals are accompanied by a large amount of noise, and the accuracy of fault diagnosis is low by directly using the original telemetry signals. In this view, this paper proposes a fault diagnosis method for spacecraft tracking telemetry and control(TT&C) systems based on principal component analysis (PCA) and residual network (ResNet). Firstly, grayscale images are generated by denoising the telemetry signals of the spacecraft TT&C system through PCA. Secondly, the images are input into the residual network to extract deep level features. Finally, the Softmax classifier is used for classification to realize the fault diagnosis of the spacecraft TT&C system. The research results show that the diagnostic accuracy of the method proposed in this paper reaches 95.34%, which is higher than other diagnostic models, and the method can be used for actual fault classification of the spacecraft TT&C system.
Published:	2023-03-28
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