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1 April 2024 Anomaly detection method for space payload parameters under telemetry command changes
Xin Jin, Yufeng He
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Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 1308112 (2024) https://doi.org/10.1117/12.3025789
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China
Abstract
To ensure the successful execution of on-orbit space missions, the operational data of space payloads must be continuously monitored. Due to the characteristics of interval-scaled variable parameters that change and fluctuate randomly with telemetry parameters, the traditional interval range anomaly detection method has low accuracy, and the deep-learning-based approach is limited in the study of parameter changes with commands. In this paper, an anomaly detection method based on LSTM network model is proposed. First, encode command (CMD) into CMD Time Series and construct the corresponding dataset. Then, generate the parameter expectation values using LSTM and design the anomaly detection discriminative parameters and rules. By training the network model and setting the thresholds of the discriminative parameters according to the performance, we demonstrate the feasibility of the method by taking the actual parameters of an experimental payload as an example.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xin Jin and Yufeng He "Anomaly detection method for space payload parameters under telemetry command changes", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 1308112 (1 April 2024); https://doi.org/10.1117/12.3025789
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KEYWORDS
Engineering
Neural networks
Performance modeling
Space operations
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