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Enhanced embedding surrogate modeling method of interactive components reliability evaluation for nose wheel steering mechanism

Author

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  • Chen, Jun-Yu
  • Feng, Yun-Wen
  • Teng, Da
  • Lu, Cheng
  • Fei, Cheng-Wei

Abstract

To ensure the continuous safety of the nose wheel steering mechanism (NWSM), it is essential to validate the reliability evaluation of interactive components. There are two challenges for efficient reliability evaluation: (i) the optimality of diverse limit state functions cannot be guaranteed due to the simultaneous extraction of multiple response samples; (ii) the errors of correlation measurement for interactive components are accumulated resulting from the failure hierarchy. To address these problems, this paper presents an enhanced embedding surrogate modeling method (EESM) by synergistically integrating the enhanced feedback surrogate model (EFSM) with embedding correlation (EC) theory. EFSM is applied to iteratively approximate the fitted limit state function to the real situation by quantifying and minimizing indicated errors level through sample screening mechanism. EC alleviates error accumulation in layered failure modeling by transforms the probability space of failure variables while preserving high-dimensional dependency structures. Taking the Nose Wheel Steering Mechanism (NWSM) as a case study, the analytical results of EESM by comparing with other reliability evaluation methods are presented as that (i) the best performance in modeling accuracy and efficiency is held by EESM, respectively at 0.3085, 4.9419 and 301 in MAE, R2 and training calls; (ii) EESM holds the superior simulation performance in calculational time and reliability, separately at 2.6809 s and 0.9746. The effort of this work promotes the improvement of reliability analysis for engineering systems with interactive components and multi-failure modes.

Suggested Citation

  • Chen, Jun-Yu & Feng, Yun-Wen & Teng, Da & Lu, Cheng & Fei, Cheng-Wei, 2025. "Enhanced embedding surrogate modeling method of interactive components reliability evaluation for nose wheel steering mechanism," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:reensy:v:262:y:2025:i:c:s0951832025004387
    DOI: 10.1016/j.ress.2025.111237
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    References listed on IDEAS

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    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. Song, Yupeng & Sun, Tao & Zhang, Zili, 2023. "Fatigue reliability analysis of floating offshore wind turbines considering the uncertainty due to finite sampling of load conditions," Renewable Energy, Elsevier, vol. 212(C), pages 570-588.
    3. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    4. Sun, Fuqiang & Fu, Fangyou & Liao, Haitao & Xu, Dan, 2020. "Analysis of multivariate dependent accelerated degradation data using a random-effect general Wiener process and D-vine Copula," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Lu, Cheng & Teng, Da & Chen, Jun-Yu & Fei, Cheng-Wei & Keshtegar, Behrooz, 2023. "Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Chen, Zhenzhong & Huang, Dongyu & Li, Xiaoke & Qiu, Guiming & Zhao, Pengcheng, 2024. "A reliability analysis method based on the intersection area division of hypersphere and paraboloid," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    7. Yuan, Kai & Sui, Xi & Zhang, Shijie & Xiao, Ning-cong & Hu, Jinghan, 2024. "AK-SYS-IE: A novel adaptive Kriging-based method for system reliability assessment combining information entropy," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    8. Zhou, Yifan & Liu, Libo & Li, Hao, 2022. "Reliability estimation and optimisation of multistate flow networks using a conditional Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Li, Guosheng & Ma, Shuaichao & Zhang, Dequan & Yang, Leping & Zhang, Weihua & Wu, Zeping, 2024. "An efficient sequential anisotropic RBF reliability analysis method with fast cross-validation and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Li, Chen & Wen, Jiong-Ran & Wan, Jing & Taylan, Osman & Fei, Cheng-Wei, 2024. "Adaptive directed support vector machine method for the reliability evaluation of aeroengine structure," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    11. Jafary, Bentolhoda & Mele, Andrew & Fiondella, Lance, 2020. "Component-based system reliability subject to positive and negative correlation," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
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