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

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    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).

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