IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v301y2022i2p747-755.html
   My bibliography  Save this article

The prediction intervals of remaining useful life based on constant stress accelerated life test data

Author

Listed:
  • Qin, Shuidan
  • Wang, Bing Xing
  • Wu, Wenhui
  • Ma, Chao

Abstract

In practice, the prediction of the remaining useful life (RUL) of the product is a very important issue. This paper considers the prediction intervals of the RUL of the product at the normal operating stress level based on the exponential or Weibull constant stress accelerated life test (CSALT) data. For the exponential CSALT with type II censoring, we derive the exact and approximate prediction intervals for the RUL of the product. Using the asymptotic normality of the maximum likelihood estimation and the bootstrap method, we provide the two prediction intervals for the RUL of the product based on the exponential CSALT type I censored data. Under the Weibull CSALT with type II censoring, we use the generalized inferential procedure to obtain the generalized prediction interval of the RUL of the product. We also get the bootstrap prediction interval of the RUL of the product based on the Weibull CSALT type I censored data. The performance of the proposed prediction intervals is assessed by Monte Carlo simulation. The simulation results show that the coverage probabilities of the proposed prediction intervals are very close to the nominal confidence level. Finally, two examples are used to illustrate the proposed methods.

Suggested Citation

  • Qin, Shuidan & Wang, Bing Xing & Wu, Wenhui & Ma, Chao, 2022. "The prediction intervals of remaining useful life based on constant stress accelerated life test data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 747-755.
    • Handle: RePEc:eee:ejores:v:301:y:2022:i:2:p:747-755
    DOI: 10.1016/j.ejor.2021.11.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221721009760
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2021.11.026?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. Zhang, Sen-Ju & Kang, Rui & Lin, Yan-Hui, 2021. "Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    2. Balakrishnan, N. & Ling, M.H., 2014. "Gamma lifetimes and one-shot device testing analysis," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 54-64.
    3. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    4. Elsayed, E.A. & Zhang, Hao, 2007. "Design of PH-based accelerated life testing plans under multiple-stress-type," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 286-292.
    5. Nasir, Ehab A. & Pan, Rong, 2015. "Simulation-based Bayesian optimal ALT designs for model discrimination," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 1-9.
    6. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    7. Wang, Bing Xing, 2009. "Testing for the validity of the assumptions in the exponential step-stress accelerated life-testing model," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2702-2709, May.
    8. Bing Wang & Keming Yu, 2009. "Optimum plan for step-stress model with progressive type-II censoring," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 115-135, May.
    9. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. René Van Dorp, J. & Mazzuchi, Thomas A., 2005. "A general Bayes weibull inference model for accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 90(2), pages 140-147.
    11. Seo, J.H. & Jung, M. & Kim, C.M., 2009. "Design of accelerated life test sampling plans with a nonconstant shape parameter," European Journal of Operational Research, Elsevier, vol. 197(2), pages 659-666, September.
    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. Seokho Moon & Hansam Cho & Eunji Koh & Yong Sung Cho & Hyoung Lok Oh & Younghoon Kim & Seoung Bum Kim, 2022. "Remanufacturing Decision-Making for Gas Insulated Switchgear with Remaining Useful Life Prediction," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    2. Essam A. Ahmed & Mahmoud El-Morshedy & Laila A. Al-Essa & Mohamed S. Eliwa, 2023. "Statistical Inference on the Entropy Measures of Gamma Distribution under Progressive Censoring: EM and MCMC Algorithms," Mathematics, MDPI, vol. 11(10), pages 1-30, May.
    3. Li, Yang & Gao, Haifeng & Chen, Hongtian & Liu, Chun & Yang, Zhe & Zio, Enrico, 2024. "Accelerated degradation testing for lifetime analysis considering random effects and the influence of stress and measurement errors," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    4. Qin, Shuidan & Wang, Bing Xing & Tsai, Tzong-Ru & Wang, Xiaofei, 2023. "The prediction of remaining useful lifetime for the Weibull k-out-of-n load-sharing system," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Catra Indra Cahyadi & Suwarno Suwarno & Aminah Asmara Dewi & Musri Kona & Muhammad Arif & Muhammad Caesar Akbar, 2023. "Solar Prediction Strategy for Managing Virtual Power Stations," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 503-512, July.

    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. Hu, Changhua & Xing, Yuanxing & Du, Dangbo & Si, Xiaosheng & Zhang, Jianxun, 2023. "Remaining useful life estimation for two-phase nonlinear degradation processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Cai, Xiao & Li, Naipeng & Xie, Min, 2024. "RUL prediction for two-phase degrading systems considering physical damage observations," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    3. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Qin, Shuidan & Wang, Bing Xing & Tsai, Tzong-Ru & Wang, Xiaofei, 2023. "The prediction of remaining useful lifetime for the Weibull k-out-of-n load-sharing system," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    5. Yu, Wennian & Shao, Yimin & Xu, Jin & Mechefske, Chris, 2022. "An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. 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).
    7. Zhang, Jian-Xun & Si, Xiao-Sheng & Du, Dang-Bo & Hu, Chang-Hua & Hu, Chen, 2020. "A novel iterative approach of lifetime estimation for standby systems with deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    8. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.
    9. Ma, Jie & Cai, Li & Liao, Guobo & Yin, Hongpeng & Si, Xiaosheng & Zhang, Peng, 2023. "A multi-phase Wiener process-based degradation model with imperfect maintenance activities," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    10. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. 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).
    12. Zhang, Jian-Xun & Du, Dang-Bo & Si, Xiao-Sheng & Hu, Chang-Hua & Zhang, Han-Wen, 2021. "Joint optimization of preventive maintenance and inventory management for standby systems with hybrid-deteriorating spare parts," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Lin, Mingqiang & You, Yuqiang & Wang, Wei & Wu, Ji, 2023. "Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    15. Si, Xiao-Sheng & Li, Tianmei & Zhang, Jianxun & Lei, Yaguo, 2022. "Nonlinear degradation modeling and prognostics: A Box-Cox transformation perspective," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    16. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    17. Finkelstein, Maxim, 2013. "On dependent items in series in different environments," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 119-122.
    18. Abdullah AH Ahmadini & Frank PA Coolen, 2020. "Statistical inference for the Arrhenius-Weibull accelerated life testing model with imprecision based on the likelihood ratio test," Journal of Risk and Reliability, , vol. 234(2), pages 275-289, April.
    19. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank PA & Coolen-Maturi, Tahani, 2023. "New reliability model for complex systems based on stochastic processes and survival signature," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1349-1364.
    20. Pang, Zhenan & Li, Tianmei & Pei, Hong & Si, Xiaosheng, 2023. "A condition-based prognostic approach for age- and state-dependent partially observable nonlinear degrading system," Reliability Engineering and System Safety, Elsevier, vol. 230(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:ejores:v:301:y:2022:i:2:p:747-755. 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: http://www.elsevier.com/locate/eor .

    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.