Deep learning-based cross-modality performance characteristics prediction method for solid rocket motor

Huixin Yang; Shangshang Zheng; Xu Wang; Mingze Xu; Xiang Li

doi:10.1117/12.3014199

8 December 2023 Deep learning-based cross-modality performance characteristics prediction method for solid rocket motor

Huixin Yang, Shangshang Zheng, Xu Wang, Mingze Xu, Xiang Li

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430A (2023) https://doi.org/10.1117/12.3014199
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

Performance indicators such as thrust and pressure of solid rocket motors (SRMs) are essential for rocket monitoring and design. However, measuring these signals requires high economic and time costs, and thrust data is difficult to measure accurately in practice. To address this challenging problem, we propose a deep learning-based cross-modal data prediction method that uses pressure data to predict the thrust data of SRMs. By building a novel RepVGG deep neural network architecture, it automatically learns features from the original data and predicts new time-series data with different modes. We verified the effectiveness of the proposed method by calculating the error between predicted and actual data, which was less than 3% as a percentage error between the predicted and actual data. The predicted data can supplement the SRM ground experiment data and reduce the cost of data measurement.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huixin Yang, Shangshang Zheng, Xu Wang, Mingze Xu, and Xiang Li "Deep learning-based cross-modality performance characteristics prediction method for solid rocket motor", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430A (8 December 2023); https://doi.org/10.1117/12.3014199

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KEYWORDS

Data modeling

Rockets

Solids

Deep learning

Performance modeling

Education and training

Convolution

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