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Decomposed-coordinated framework with intelligent extremum network for operational reliability analysis of complex system

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  • Jia-Qi, Liu
  • Yun-Wen, Feng
  • Cheng, Lu
  • Wei-Huang, Pan

Abstract

The analysis of operational reliability in complex systems, which involve numerous subsystems and multiple disciplines, presents significant computational challenges due to their highly nonlinear, transient nature and the presence of many hyperparameters. Although reliability analysis models have made progress, they are still inadequate for accurately modeling composite functions with multiple sublayers and sub-functions. To improve the performance of modeling composite functions, the decomposed-coordinated intelligent extremum network model (DC-IENM) is proposed in this paper. The present study employs the decomposed-coordinated (DC) strategy as a means to effectively address the coordination relationship among multiple analysis objectives. To assess the efficacy of the proposed approach, two illustrative examples are considered: (1) the approximate and probabilistic analysis of a nonlinear function with multiple responses, and (2) the reliability analysis of civil aircraft brake system temperature. These examples serve to demonstrate the effectiveness of the developed DC-IENM. Furthermore, the modeling and simulation properties are rigorously examined by means of a comparative analysis involving various methodologies. The obtained results unequivocally indicate that the proposed DC-IENM exhibits distinct advantages in terms of both computational efficiency and precision.

Suggested Citation

  • Jia-Qi, Liu & Yun-Wen, Feng & Cheng, Lu & Wei-Huang, Pan, 2024. "Decomposed-coordinated framework with intelligent extremum network for operational reliability analysis of complex system," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:reensy:v:242:y:2024:i:c:s095183202300666x
    DOI: 10.1016/j.ress.2023.109752
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    References listed on IDEAS

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    1. Lee, Juseong & Mitici, Mihaela, 2020. "An integrated assessment of safety and efficiency of aircraft maintenance strategies using agent-based modelling and stochastic Petri nets," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. Jia-Qi Liu & Yun-Wen Feng & Xiao-Feng Xue & Cheng Lu, 2021. "Intelligent Extremum Surrogate Modeling Framework for Dynamic Probabilistic Analysis of Complex Mechanism," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, February.
    3. Pan, Wei-Huang & Feng, Yun-Wen & Lu, Cheng & Liu, Jia-Qi, 2023. "Analyzing the operation reliability of aeroengine using Quick Access Recorder flight data," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    4. Oguzhan Yılmaz & Eren Bas & Erol Egrioglu, 2022. "The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1699-1711, April.
    5. Gao, Shuzhi & Zhang, Sixuan & Zhang, Yimin & Gao, Yue, 2020. "Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    6. Jamshid Piri & Mohammad Abdolahipour & Behrooz Keshtegar, 2023. "Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 683-712, January.
    7. Luo, Changqi & Zhu, Shun-Peng & Keshtegar, Behrooz & Niu, Xiaopeng & Taylan, Osman, 2023. "An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Meng, Debiao & Yang, Shiyuan & Jesus, Abílio M.P. de & Zhu, Shun-Peng, 2023. "A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower," Renewable Energy, Elsevier, vol. 203(C), pages 407-420.
    9. 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).
    10. Chen, Jun-Yu & Feng, Yun-Wen & Teng, Da & Lu, Cheng & Fei, Cheng-Wei, 2022. "Support vector machine-based similarity selection method for structural transient reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    11. Dong, Zhe & Li, Bowen & Li, Junyi & Huang, Xiaojin & Zhang, Zuoyi, 2022. "Online reliability assessment of energy systems based on a high-order extended-state-observer with application to nuclear reactors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    12. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    13. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. Xiao, Sinan & Oladyshkin, Sergey & Nowak, Wolfgang, 2020. "Reliability analysis with stratified importance sampling based on adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
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