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Advancing spacecraft safety and longevity: A review of guided waves-based structural health monitoring

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  • Yu, Sunquan
  • Luo, Kai
  • Fan, Chengguang
  • Fu, Kangjia
  • Wu, Xuesong
  • Chen, Yong
  • Zhang, Xiang

Abstract

The reusability and prolonged operation of spacecraft underscore the critical need for advanced structural health monitoring (SHM) systems. Guided waves-based SHM (GWs-SHM) offers an effective solution with its comprehensive coverage, enhanced sensitivity, and real-time monitoring capabilities, addressing the imperative for rapid anomaly detection and fault diagnosis. This study examines the application of GWs-SHM in spacecraft, focusing on the localization of space debris impacts, structural damage assessment, and leak detection. It discusses the challenges faced by spacecraft components and emphasizes the need for sophisticated SHM systems. Recent theoretical and methodological advancements in guided wave modeling and simulation are also reviewed. The paper further explores the integration of GWs-SHM with emerging aerospace technologies, such as space robots, artificial intelligence, multi-sensors data fusion, guided-wave based wireless communication and energy transmission. This work envisions significant advancements in spacecraft safety and operational longevity through the development of cutting-edge GWs-SHM technologies, urging for continuous innovation in sensor technology, algorithm development, and the integration of artificial intelligence for smarter decision-making in the challenging space environment.

Suggested Citation

  • Yu, Sunquan & Luo, Kai & Fan, Chengguang & Fu, Kangjia & Wu, Xuesong & Chen, Yong & Zhang, Xiang, 2025. "Advancing spacecraft safety and longevity: A review of guided waves-based structural health monitoring," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    • Handle: RePEc:eee:reensy:v:254:y:2025:i:pa:s0951832024006574
    DOI: 10.1016/j.ress.2024.110586
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    References listed on IDEAS

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    1. Giannakeas, Ilias N. & Mazaheri, Fatemeh & Bacarreza, Omar & Khodaei, Zahra Sharif & Aliabadi, Ferri M.H., 2023. "Probabilistic residual strength assessment of smart composite aircraft panels using guided waves," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Kim, Wongon & Lee, Guesuk & Son, Hyejeong & Choi, Hyunhee & Youn, Byeng D., 2022. "Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Miele, S. & Karve, P. & Mahadevan, S., 2023. "Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Ge, Xiangyu & Zhou, Qianxiang & Liu, Zhongqi, 2020. "Assessment of space station on-orbit maintenance task complexity," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    5. Hu, Yunpeng & Peng, Qibo & Ni, Qing & Wu, Xinfeng & Ye, Dongming, 2023. "Event-based safety and reliability analysis integration in model-based space mission design," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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