# Fault Prediction for Rotating Mechanism of Satellite Based on SSA and Improved Informer
## 5 Conclusions  
Based on SSA and an improved version of the informer, this paper proposes a method for predicting satellite rotating mechanism faults using telemetry data and validates this method through experimental tests. The main contributions of this study are as follows. Firstly, experimental validation was performed using rotational mechanism data from on-orbit satellite telemetry. By studying the working principles and background knowledge of the rotational mechanisms, we propose a method for fault prediction in satellite rotational mechanisms based on the relationship between the turntable motor current and the turntable temperature. Secondly, during data processing, to address the issue of significant background noise interference in telemetry signals, we propose the use of SSA to decompose and reconstruct the telemetry data. Using this method, the original telemetry data were decomposed into five sub-sequences, which were then reconstructed based on their eigenvalue contribution rates. This approach retains the trend and oscillatory components while removing the noise components, thereby enhancing the accuracy of subsequent models in predicting turntable temperature data. Thirdly, for time-series signal prediction, a fault prediction method for satellite turntables based on SSA and an improved informer model was proposed. This method combines the exceptional signal decomposition and reconstruction capabilities of SSA with the outstanding time-series forecasting capabilities of CNN-Informer, thereby further enhancing the practicality and reliability of the prediction results. Lastly, multiple comparative experiments were conducted to validate the proposed method for predicting satellite telemetry data. The experimental results indicate that the proposed method achieved a MAE of 0.405 and a MSE of 0.086, representing at least a 10% improvement over other methods. In summary, this paper focused on fault prediction for satellite rotational mechanisms. Experimental results on the satellite telemetry dataset demonstrated that the proposed model achieves higher and more stable prediction accuracy compared to the benchmark models. This indicates that the model is effective in performing fault prediction tasks for satellite rotational mechanisms. Our future work will explore the application of this model to other related satellite subsystems and even industrial equipment for fault prediction. Conceptualization, Q.L. and Y.Z. ([PERSON]); methodology, Q.L. and B.L. ; software, Y.Z. ([PERSON]); validation, Q.L. and Y.L. ; formal analysis, Q.L. ; investigation, Y.Z. ([PERSON]); resources, Y.Z. ([PERSON]); data curation, Y.L. ; writing--original draft preparation, Q.L. ; writing--review and editing, Q.L. ; visualization, Y.Z. ([PERSON]); project administration, Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Excellent Fund of Youth Innovation Promotion Association of Chinese Academy of Sciences, grant number Y2022078. Not applicable. Figure 11: Schematic diagram of turntable temperature threshold generation and fault prediction. * (26) [PERSON]; [PERSON]; [PERSON] Water Level Prediction Based on SSA-LSTM Model. In Proceedings of the 2022 7 th International Conference on Computational Intelligence and Applications (ICCIA), Nanjing, China, 24-26 June 2022; pp. 108-112. * (27) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event, 2-9 February 2021; Volume 35, pp. 11106-11115. * (28) [PERSON]; [PERSON] CNN-Informer-Based Remaining Useful Life Prediction for Electrical Devices. In Proceedings of the 2022 4 th International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China, 24-27 August 2022; pp. 1-6. * (29) [PERSON]; [PERSON] Long-Term Structural State Trend Forecasting Based on an FFT-Informer Model. _Appl. Sci._**2023**, _13_, 2553.