IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v152y2016icp166-175.html
   My bibliography  Save this article

Time-variant reliability assessment through equivalent stochastic process transformation

Author

Listed:
  • Wang, Zequn
  • Chen, Wei

Abstract

Time-variant reliability measures the probability that an engineering system successfully performs intended functions over a certain period of time under various sources of uncertainty. In practice, it is computationally prohibitive to propagate uncertainty in time-variant reliability assessment based on expensive or complex numerical models. This paper presents an equivalent stochastic process transformation approach for cost-effective prediction of reliability deterioration over the life cycle of an engineering system. To reduce the high dimensionality, a time-independent reliability model is developed by translating random processes and time parameters into random parameters in order to equivalently cover all potential failures that may occur during the time interval of interest. With the time-independent reliability model, an instantaneous failure surface is attained by using a Kriging-based surrogate model to identify all potential failure events. To enhance the efficacy of failure surface identification, a maximum confidence enhancement method is utilized to update the Kriging model sequentially. Then, the time-variant reliability is approximated using Monte Carlo simulations of the Kriging model where system failures over a time interval are predicted by the instantaneous failure surface. The results of two case studies demonstrate that the proposed approach is able to accurately predict the time evolution of system reliability while requiring much less computational efforts compared with the existing analytical approach.

Suggested Citation

  • Wang, Zequn & Chen, Wei, 2016. "Time-variant reliability assessment through equivalent stochastic process transformation," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 166-175.
  • Handle: RePEc:eee:reensy:v:152:y:2016:i:c:p:166-175
    DOI: 10.1016/j.ress.2016.02.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832016000491
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2016.02.008?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. Crestaux, Thierry & Le Maıˆtre, Olivier & Martinez, Jean-Marc, 2009. "Polynomial chaos expansion for sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1161-1172.
    2. Wang, Zequn & Wang, Pingfeng, 2013. "A new approach for reliability analysis with time-variant performance characteristics," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 70-81.
    3. Hu, Zhen & Du, Xiaoping, 2012. "Reliability analysis for hydrokinetic turbine blades," Renewable Energy, Elsevier, vol. 48(C), pages 251-262.
    4. Breitung, Karl, 1988. "Asymptotic crossing rates for stationary Gaussian vector processes," Stochastic Processes and their Applications, Elsevier, vol. 29(2), pages 195-207, September.
    5. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    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. Chen, Junhua & Chen, Longmiao & Qian, Linfang & Chen, Guangsong & Zhou, Shijie, 2022. "Time-dependent kinematic reliability analysis of gear mechanism based on sequential decoupling strategy and saddle-point approximation," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2024. "A Subdomain uncertainty-guided Kriging method with optimized feasibility metric for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Jiang, Chen & Yan, Yifang & Wang, Dapeng & Qiu, Haobo & Gao, Liang, 2021. "Global and local Kriging limit state approximation for time-dependent reliability-based design optimization through wrong-classification probability," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    4. Das, Sourav & Tesfamariam, Solomon, 2024. "Reliability assessment of stochastic dynamical systems using physics informed neural network based PDEM," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    5. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    6. Cheng, Kai & Lu, Zhenzhou, 2019. "Time-variant reliability analysis based on high dimensional model representation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 310-319.
    7. Ouyang, Linhan & Che, Yushuai & Park, Chanseok & Chen, Yuejian, 2024. "A novel active learning Gaussian process modeling-based method for time-dependent reliability analysis considering mixed variables," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    8. Zhang, Yang & Xu, Jun & Gardoni, Paolo, 2024. "A loading contribution degree analysis-based strategy for time-variant reliability analysis of structures under multiple loading stochastic processes," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Song, Zhouzhou & Zhang, Hanyu & Liu, Zhao & Zhu, Ping, 2023. "A two-stage Kriging estimation variance reduction method for efficient time-variant reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Zhang, Yang & Xu, Jun & Beer, Michael, 2023. "A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    11. Qian, Hua-Ming & Li, Yan-Feng & Huang, Hong-Zhong, 2021. "Time-variant system reliability analysis method for a small failure probability problem," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    12. Zhao, Zhao & Zhao, Yan-Gang & Li, Pei-Pei, 2023. "A novel decoupled time-variant reliability-based design optimization approach by improved extreme value moment method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Yang, Seonghyeok & Jo, Hwisang & Lee, Kyungeun & Lee, Ikjin, 2022. "Expected system improvement (ESI): A new learning function for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    14. Wang, Dapeng & Qiu, Haobo & Gao, Liang & Jiang, Chen, 2021. "A single-loop Kriging coupled with subset simulation for time-dependent reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    16. Wang, Zhonglai & Liu, Jing & Yu, Shui, 2020. "Time-variant reliability prediction for dynamic systems using partial information," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    17. Tengda Xin & Jiguang Zhao & Cunyan Cui & Yongsheng Duan, 2020. "A non-probabilistic time-variant method for structural reliability analysis," Journal of Risk and Reliability, , vol. 234(5), pages 664-675, October.
    18. 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).
    19. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.
    20. Yan Shi & Zhenzhou Lu & Ruyang He, 2020. "Advanced time-dependent reliability analysis based on adaptive sampling region with Kriging model," Journal of Risk and Reliability, , vol. 234(4), pages 588-600, August.
    21. Zhou, Di & Pan, Ershun & Zhang, Xufang & Zhang, Yimin, 2020. "Dynamic Model-based Saddle-point Approximation for Reliability and Reliability-based Sensitivity Analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(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. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    2. 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).
    3. Hawchar, Lara & El Soueidy, Charbel-Pierre & Schoefs, Franck, 2017. "Principal component analysis and polynomial chaos expansion for time-variant reliability problems," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 406-416.
    4. Wang, Zeyu & Shafieezadeh, Abdollah, 2020. "Real-time high-fidelity reliability updating with equality information using adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    5. Brown, S. & Beck, J. & Mahgerefteh, H. & Fraga, E.S., 2013. "Global sensitivity analysis of the impact of impurities on CO2 pipeline failure," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 43-54.
    6. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    7. Chen, Xin & Molina-Cristóbal, Arturo & Guenov, Marin D. & Riaz, Atif, 2019. "Efficient method for variance-based sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 97-115.
    8. Melito, Gian Marco & Müller, Thomas Stephan & Badeli, Vahid & Ellermann, Katrin & Brenn, Günter & Reinbacher-Köstinger, Alice, 2021. "Sensitivity analysis study on the effect of the fluid mechanics assumptions for the computation of electrical conductivity of flowing human blood," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Oladyshkin, S. & Nowak, W., 2012. "Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 179-190.
    10. Wu, Zeping & Wang, Donghui & Okolo N, Patrick & Hu, Fan & Zhang, Weihua, 2016. "Global sensitivity analysis using a Gaussian Radial Basis Function metamodel," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 171-179.
    11. Anstett-Collin, F. & Goffart, J. & Mara, T. & Denis-Vidal, L., 2015. "Sensitivity analysis of complex models: Coping with dynamic and static inputs," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 268-275.
    12. Horiguchi, Akira & Pratola, Matthew T. & Santner, Thomas J., 2021. "Assessing variable activity for Bayesian regression trees," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    13. Alexanderian, Alen & Gremaud, Pierre A. & Smith, Ralph C., 2020. "Variance-based sensitivity analysis for time-dependent processes," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    14. Steiner, M. & Bourinet, J.-M. & Lahmer, T., 2019. "An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 323-340.
    15. Beccacece, Francesca & Borgonovo, Emanuele & Buzzard, Greg & Cillo, Alessandra & Zionts, Stanley, 2015. "Elicitation of multiattribute value functions through high dimensional model representations: Monotonicity and interactions," European Journal of Operational Research, Elsevier, vol. 246(2), pages 517-527.
    16. Haro Sandoval, Eduardo & Anstett-Collin, Floriane & Basset, Michel, 2012. "Sensitivity study of dynamic systems using polynomial chaos," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 15-26.
    17. Oladyshkin, Sergey & Nowak, Wolfgang, 2018. "Incomplete statistical information limits the utility of high-order polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 137-148.
    18. Constantine, Paul G. & Diaz, Paul, 2017. "Global sensitivity metrics from active subspaces," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 1-13.
    19. Wu, Zeping & Wang, Wenjie & Wang, Donghui & Zhao, Kun & Zhang, Weihua, 2019. "Global sensitivity analysis using orthogonal augmented radial basis function," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 291-302.
    20. El Moçayd, Nabil & Seaid, Mohammed, 2021. "Data-driven polynomial chaos expansions for characterization of complex fluid rheology: Case study of phosphate slurry," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

    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:152:y:2016:i:c:p:166-175. 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.