Abstract: This paper proposes a fault data generation method for satellite turntables based on a diffusion model and the Informer architecture, to address the issue of limited fault data samples in satellite telemetry, which restricts the performance of fault detection and diagnosis models. By integrating time series decomposition with deep learning models, the proposed method aims to generate high quality and interpretable time series data. Through data decomposition, the forward diffusion process achieves component decoupling, improving the diversity and accuracy of the generated data. During the reverse generation phase, the Informer decoder leverages a sparse self-attention mechanism to effectively capture long-term dependencies and complex temporal patterns. The method is suitable for both unconditional and conditional fault data generation, including scenarios with missing data. Experimental results demonstrate that the proposed method significantly outperforms Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in terms of metrics such as Mean Squared Error (MSE), Correlation Score (CS), and Correlation Coefficient (R).

Key words: satellite turntable, telemetry data, diffusion model, time series decomposition, fault data generation