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Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis

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  • Dong, Yutong
  • Jiang, Hongkai
  • Wu, Zhenghong
  • Yang, Qiao
  • Liu, Yunpeng

Abstract

Hypersonic flight vehicle (HFV) with long term exposure to poor operating environments will inevitably experience performance degradation and potential failures. Currently, data-driven approaches have been commonly used for fault diagnosis. However, it is a challenge to obtain reliable and adequate data to identify HFV faults. To cope with this issue, this paper put forward a digital twin-assisted multiscale residual-self-attention feature fusion network (MRFFN) for hypersonic flight vehicle fault diagnosis. Firstly, a mathematical simulation is performed to establish the DT model of HFV. Then, the constructed DT model is employed for simulating multiple fault states of HFV to generate an approximation to the real system state data. Finally, a novel MRFFN is designed for training and validation utilizing the data derived from the DT model. The comparison performance demonstrates the MRFFN is superior to other intelligence methods in its ability to accurately identify hypersonic flight vehicle faults.

Suggested Citation

  • Dong, Yutong & Jiang, Hongkai & Wu, Zhenghong & Yang, Qiao & Liu, Yunpeng, 2023. "Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001680
    DOI: 10.1016/j.ress.2023.109253
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    References listed on IDEAS

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    1. Wang, Xu & Shen, Changqing & Xia, Min & Wang, Dong & Zhu, Jun & Zhu, Zhongkui, 2020. "Multi-scale deep intra-class transfer learning for bearing fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    3. Xia, Min & Shao, Haidong & Williams, Darren & Lu, Siliang & Shu, Lei & de Silva, Clarence W., 2021. "Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    4. Tang, Shengnan & Zhu, Yong & Yuan, Shouqi, 2022. "Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
    7. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Dually attentive multiscale networks for health state recognition of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
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    Cited by:

    1. Zio, Enrico & Miqueles, Leonardo, 2024. "Digital twins in safety analysis, risk assessment and emergency management," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. Wang, Jun & Ren, He & Shen, Changqing & Huang, Weiguo & Zhu, Zhongkui, 2024. "Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Dong, Yutong & Jiang, Hongkai & Yao, Renhe & Mu, Mingzhe & Yang, Qiao, 2024. "Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

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