IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v225y2022ics0951832022002241.html
   My bibliography  Save this article

3-Dimensional general ADT modeling and analysis: Considering epistemic uncertainties in unit, time and stress dimension

Author

Listed:
  • Li, Xiao-Yang
  • Chen, Da-Yu
  • Wu, Ji-Peng
  • Kang, Rui

Abstract

Accelerated degradation testing (ADT) has been widely used to identify the performance degradation law so as to evaluate the reliability and lifetime. During this process, there are aleatory and epistemic uncertainties in the unit, time and stress dimension. However, the current studies do not have a comprehensive modeling framework and a general ADT model that fully considers and properly quantifies all sources and types of uncertainties. Aiming at these problems, this paper proposes a 3-Dimesional modeling framework and a general ADT model, and gives a new uncertain ADT model considering epistemic uncertainties in the unit, and stress dimension. Furthermore, considering the weights of observed degradation data under different stress levels, this paper proposes a new parameter estimation method, uncertain weighted least squares, to well predict degradation law and control uncertainties. Finally, a microwave case, a rubber seal case and two simulation cases are conducted to show the practicability of the proposed framework and model. The results show that the proposed framework can guide the construction of different models according to different situations; and compared with current methods, the proposed model are more appropriate for performing ADT model and quantifying uncertainties when uncertainty in the stress dimension cannot be ignored.

Suggested Citation

  • Li, Xiao-Yang & Chen, Da-Yu & Wu, Ji-Peng & Kang, Rui, 2022. "3-Dimensional general ADT modeling and analysis: Considering epistemic uncertainties in unit, time and stress dimension," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
  • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002241
    DOI: 10.1016/j.ress.2022.108577
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022002241
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2022.108577?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. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. 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).
    3. Le Liu & Xiao-Yang Li & Enrico Zio & Rui Kang & Tong-Min Jiang, 2017. "Model Uncertainty in Accelerated Degradation Testing Analysis," Post-Print hal-01652218, HAL.
    4. Wu, Ji-Peng & Kang, Rui & Li, Xiao-Yang, 2020. "Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    5. Pan, Donghui & Liu, Jia-Bao & Yang, Wenzhi, 2018. "A new result on lifetime estimation based on skew-Wiener degradation model," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 157-164.
    6. Lin, Chun Pang & Ling, Man Ho & Cabrera, Javier & Yang, Fangfang & Yu, Denis Yau Wai & Tsui, Kwok Leung, 2021. "Prognostics for lithium-ion batteries using a two-phase gamma degradation process model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    7. Zhang, Ruiyuan & Min, Ting & Chen, Li & Kang, Qinjun & He, Ya-Ling & Tao, Wen-Quan, 2019. "Pore-scale and multiscale study of effects of Pt degradation on reactive transport processes in proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    8. Dong, Qinglai & Cui, Lirong, 2019. "A study on stochastic degradation process models under different types of failure Thresholds," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 202-212.
    9. Ye, Zhi-Sheng & Chen, Nan & Shen, Yan, 2015. "A new class of Wiener process models for degradation analysis," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 58-67.
    Full references (including those not matched with items on IDEAS)

    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. Peihua Jiang, 2022. "Statistical Inference of Wiener Constant-Stress Accelerated Degradation Model with Random Effects," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    2. Asgari, Ali & Si, Wujun & Yuan, Liang & Krishnan, Krishna & Wei, Wei, 2024. "Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Wang, Xiaofei & Wang, Bing Xing & Jiang, Pei Hua & Hong, Yili, 2020. "Accurate reliability inference based on Wiener process with random effects for degradation data," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Chen, Wen-Bin & Li, Xiao-Yang & Kang, Rui, 2022. "Integration for degradation analysis with multi-source ADT datasets considering dataset discrepancies and epistemic uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Hui-Ying Wang & Zhao-Qiang Wang, 2022. "A condition-based preventive replacement policy with imperfect manual inspection for a two-stage deterioration process," Journal of Risk and Reliability, , vol. 236(2), pages 225-236, April.
    6. Zheng, Huiling & Kong, Xuefeng & Xu, Houbao & Yang, Jun, 2021. "Reliability analysis of products based on proportional hazard model with degradation trend and environmental factor," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Song, Kai & Cui, Lirong, 2022. "A common random effect induced bivariate gamma degradation process with application to remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Liu, Zhe & Li, Xiaoyang & Kang, Rui, 2022. "Uncertain differential equation based accelerated degradation modeling," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    9. Liu, Di & Wang, Shaoping & Zhang, Chao & Tomovic, Mileta, 2018. "Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 25-38.
    10. Jiang, Deyin & Chen, Tianyu & Xie, Juanzhang & Cui, Weimin & Song, Bifeng, 2023. "A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Wang, Zhijie & Zhai, Qingqing & Chen, Piao, 2021. "Degradation modeling considering unit-to-unit heterogeneity-A general model and comparative study," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Hao, Songhua & Yang, Jun & Berenguer, Christophe, 2019. "Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 261-270.
    13. Liu, Di & Wang, Shaoping, 2020. "A degradation modeling and reliability estimation method based on Wiener process and evidential variable," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    14. Ye, Xuerong & Hu, Yifan & Zheng, Bokai & Chen, Cen & Zhai, Guofu, 2022. "A new class of multi-stress acceleration models with interaction effects and its extension to accelerated degradation modelling," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    15. Pang, Zhenan & Si, Xiaosheng & Hu, Changhua & Du, Dangbo & Pei, Hong, 2021. "A Bayesian Inference for Remaining Useful Life Estimation by Fusing Accelerated Degradation Data and Condition Monitoring Data," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    16. Li, Zan & Wang, Fengming & Wang, Chengjie & Hu, Qingpei & Yu, Dan, 2021. "Reliability modeling and evaluation of lifetime delayed degradation process with nondestructive testing," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    17. Weichao Yu & Xianbin Zheng & Weihe Huang & Qingwen Cai & Jie Guo & Jili Xu & Yang Liu & Jing Gong & Hong Yang, 2022. "A Data-Driven Methodology for the Reliability Analysis of the Natural Gas Compressor Unit Considering Multiple Failure Modes," Energies, MDPI, vol. 15(10), pages 1-18, May.
    18. Zhang, Jian-Xun & Hu, Chang-Hua & He, Xiao & Si, Xiao-Sheng & Liu, Yang & Zhou, Dong-Hua, 2017. "Lifetime prognostics for deteriorating systems with time-varying random jumps," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 338-350.
    19. Chen, Xiaowu & Liu, Zhen, 2022. "A long short-term memory neural network based Wiener process model for remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    20. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(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:225:y:2022:i:c:s0951832022002241. 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.