A multi-head attention network with adaptive meta-transfer learning for RUL prediction of rocket engines

Tongyang Pan, Jinglong Chen, Zhisheng Ye and Aimin Li

Reliability Engineering and System Safety, 2022, vol. 225, issue C

Abstract: Accurate prediction of remaining useful life (RUL) is necessary to ensure stable and safe operations for rocket engines. The paper proposed a multi-head attention network coupled with adaptive meta-transfer learning for RUL prediction. By combining the convolution-based branch with an attention-based branch, the multi-head attention network is proposed for accurate RUL prediction of cryogenic bearings in rocket engines under the steady stage. In addition, an adaptive model-agnostic meta-transfer learning strategy is developed to further improve the performance under small sample circumstances with adaptive hyper-parameters. To demonstrate the superiority, the proposed method is compared with typical benchmark algorithms using real monitoring data from a high-precision cryogenic rocket engine experiment platform. Results indicate that the proposed method achieves better performance compared with existing models under multiple evaluation indexes.

Keywords: Remaining useful life; Deep neural network; Rocket engine; Transfer learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002538

DOI: 10.1016/j.ress.2022.108610

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