IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v234y2023ics0951832023000637.html
   My bibliography  Save this article

Adaptive vectorial surrogate modeling framework for multi-objective reliability estimation

Author

Listed:
  • Lu, Cheng
  • Teng, Da
  • Chen, Jun-Yu
  • Fei, Cheng-Wei
  • Keshtegar, Behrooz

Abstract

Vectorial modeling concept is proposed in this paper by introducing the matrix theory into the point modeling concept (surrogate modeling strategy), and an adaptive vectorial surrogate modeling framework (AVSMF, short for) is developed based on the vectorial modeling concept and adaptive modeling strategy. Herein, the adaptive modeling strategy is adopted to determine the form of mathematical model of each objective in line with the cost function, the surrogate modeling strategy is regarded as the basis function for reflecting the relationship of the output of single-objective between the relevant inputs, and the matrix theory is used to ascertain the vectors and cell arrays of undetermined parameters and to establish the performance function of multi-objective structures. To validate the proposed method, we use three examples including approximate and probabilistic analysis of nonlinear function with multiple responses, reliability evaluation of landing gear brake system temperature and reliability assessment of aeroengine high-pressure turbine blisk stress, strain and deformation, to demonstrate the effectiveness of the developed AVSMF. Besides, the modeling and simulation properties are verified by comparison of different methods. The results show that the proposed AVSMF has obvious advantages in the computational efficiency and precision.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000637
    DOI: 10.1016/j.ress.2023.109148
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023000637
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109148?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zuhal, Lavi Rizki & Faza, Ghifari Adam & Palar, Pramudita Satria & Liem, Rhea Patricia, 2021. "On dimensionality reduction via partial least squares for Kriging-based reliability analysis with active learning," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    3. Zhang, Kun & Chen, Ning & Zeng, Peng & Liu, Jian & Beer, Michael, 2022. "An efficient reliability analysis method for structures with hybrid time-dependent uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    4. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    5. Mingxuan Zhao & Yuhan Liu & Dan A. Ralescu & Jian Zhou, 2018. "The covariance of uncertain variables: definition and calculation formulae," Fuzzy Optimization and Decision Making, Springer, vol. 17(2), pages 211-232, June.
    6. Chen, Qian & Zuo, Lili & Wu, Changchun & Cao, Yankai & Bu, Yaran & Chen, Feng & Sadiq, Rehan, 2021. "Supply reliability assessment of a gas pipeline network under stochastic demands," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    7. Ait-Sidi-Allal, M. L. & Baccini, A. & Mondot, A. M., 2004. "A new algorithm for estimating the parameters and their asymptotic covariance in correlation and association models," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 389-421, April.
    8. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab, 2021. "Offshore system safety and reliability considering microbial influenced multiple failure modes and their interdependencies," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Sierra, Gina & Robinson, Elinirina I. & Goebel, Kai, 2021. "Improving tail accuracy of the predicted cumulative distribution function of time of failure," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    10. Acharki, Naoufal & Bertoncello, Antoine & Garnier, Josselin, 2023. "Robust prediction interval estimation for Gaussian processes by cross-validation method," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    11. Dai, Baorui & Xia, Ye & Li, Qi, 2022. "An extreme value prediction method based on clustering algorithm," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    12. Gao, Haifeng & Wang, Anjenq & Zio, Enrico & Bai, Guangchen, 2020. "An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    13. Nielsen, Adam, 2016. "The Monte Carlo computation error of transition probabilities," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 163-170.
    14. Khatibinia, Mohsen & Javad Fadaee, Mohammad & Salajegheh, Javad & Salajegheh, Eysa, 2013. "Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 22-33.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Teng, Da & Feng, Yun-Wen & Chen, Jun-Yu & Liu, Jia-Qi & Lu, Cheng, 2024. "Multi-polynomial chaos Kriging-based adaptive moving strategy for comprehensive reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Jia-Qi, Liu & Yun-Wen, Feng & Da, Teng & Jun-Yu, Chen & Cheng, Lu, 2023. "Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Shaochen & Tian, Wende & Li, Chuankun & Cui, Zhe & Liu, Bin, 2023. "Mechanism-based deep learning for tray efficiency soft-sensing in distillation process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    2. Zhao, Yunjie & Cheng, Xi & Zhang, Taihong & Wang, Lei & Shao, Wei & Wiart, Joe, 2023. "A global–local attention network for uncertainty analysis of ground penetrating radar modeling," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Nagode, Marko & Oman, Simon & Klemenc, Jernej & Panić, Branislav, 2023. "Gumbel mixture modelling for multiple failure data," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Chang, Qi & Zhou, Changcong & Wei, Pengfei & Zhang, Yishang & Yue, Zhufeng, 2021. "A new non-probabilistic time-dependent reliability model for mechanisms with interval uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Wang, Zihan & Daeipour, Mohamad & Xu, Hongyi, 2023. "Quantification and propagation of Aleatoric uncertainties in topological structures," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    6. Wang, Tianzhe & Chen, Zequan & Li, Guofa & He, Jialong & Liu, Chao & Du, Xuejiao, 2024. "A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    7. Lin Wang & Yuping Xing, 2022. "Risk Assessment of a Coupled Natural Gas and Electricity Market Considering Dual Interactions: A System Dynamics Model," Energies, MDPI, vol. 16(1), pages 1-18, December.
    8. Yaping Li & Enrico Zio & Ershun Pan, 2021. "An MEWMA-based segmental multivariate hidden Markov model for degradation assessment and prediction," Journal of Risk and Reliability, , vol. 235(5), pages 831-844, October.
    9. Zhongfeng Qin & Qiqi Li, 2023. "An uncertain support vector machine with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 611-629, December.
    10. Zheng, Xiao-Wei & Li, Hong-Nan & Gardoni, Paolo, 2023. "Hybrid Bayesian-Copula-based risk assessment for tall buildings subject to wind loads considering various uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    11. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    12. Li, Chao & Diao, Yucheng & Li, Hong-Nan & Pan, Haiyang & Ma, Ruisheng & Han, Qiang & Xing, Yihan, 2023. "Seismic performance assessment of a sea-crossing cable-stayed bridge system considering soil spatial variability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    13. Yazdi, Mohammad & Khan, Faisal & Abbassi, Rouzbeh & Quddus, Noor & Castaneda-Lopez, Homero, 2022. "A review of risk-based decision-making models for microbiologically influenced corrosion (MIC) in offshore pipelines," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    14. Krupenev, Dmitry & Boyarkin, Denis & Iakubovskii, Dmitrii, 2020. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    15. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
    16. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.
    17. Ding, Yueting & Chen, Sai & Zheng, Yilei & Chai, Shanglei & Nie, Rui, 2022. "Resilience assessment of China's natural gas system under supply shortages: A system dynamics approach," Energy, Elsevier, vol. 247(C).
    18. Wang, WuChang & Zhang, Yi & Li, YuXing & Hu, Qihui & Liu, Chengsong & Liu, Cuiwei, 2022. "Vulnerability analysis method based on risk assessment for gas transmission capabilities of natural gas pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    19. Feng, Liuyang & Zhang, Limao, 2022. "Enhanced prediction intervals of tunnel-induced settlement using the genetic algorithm and neural network," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    20. Dai, Hongzhe & Zhang, Boyi & Wang, Wei, 2015. "A multiwavelet support vector regression method for efficient reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 132-139.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:234:y:2023:i:c:s0951832023000637. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.