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A reliability estimation approach via Wiener degradation model with measurement errors

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  • Pan, Donghui
  • Wei, Yantao
  • Fang, Houzhang
  • Yang, Wenzhi

Abstract

This paper proposes a reliability estimation approach based on EM algorithm and Wiener processes by considering measurement errors. Firstly, the time-transformed Wiener processes are used to model the degradation process of the product, which simultaneously consider the temporal variability, unit-to-unit heterogeneity and measurement errors. In addition, we obtain the closed-form expressions of some reliability quantities such as reliability function and probability density function of the life. Moreover, the expectation maximization algorithm is adopted to estimate the model parameters effectively. Finally, a numerical example and a practical case study for LED lamps are provided to illustrate the effectiveness and superiority of the presented approach.

Suggested Citation

  • Pan, Donghui & Wei, Yantao & Fang, Houzhang & Yang, Wenzhi, 2018. "A reliability estimation approach via Wiener degradation model with measurement errors," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 131-141.
    • Handle: RePEc:eee:apmaco:v:320:y:2018:i:c:p:131-141
    DOI: 10.1016/j.amc.2017.09.020
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    References listed on IDEAS

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    Cited by:

    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. 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.
    3. Liu, Di & Wang, Shaoping & Zhang, Chao, 2022. "Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    4. Ma, Zhonghai & Wang, Shaoping & Ruiz, Cesar & Zhang, Chao & Liao, Haitao & Pohl, Edward, 2020. "Reliability estimation from two types of accelerated testing data considering measurement error," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    5. Xu, Qinqin & Zhu, Yuanguo, 2023. "Reliability analysis of uncertain random systems based on uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    6. Liu, Di & Wang, Shaoping, 2021. "Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    7. Zheng, Bokai & Chen, Cen & Lin, Yigang & Hu, Yifan & Ye, Xuerong & Zhai, Guofu & Zio, Enrico, 2022. "Optimal design of step-stress accelerated degradation test oriented by nonlinear and distributed degradation process," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Liu, Di & Wang, Shaoping & Cui, Xiaoyu, 2022. "An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    9. Tingting Huang & Songming Chen & Yuepu Zhao & Wei Dai, 2023. "Reliability assessment of degradation processes with random shocks considering recoverable shock damages," Journal of Risk and Reliability, , vol. 237(6), pages 1150-1162, December.
    10. Peihua Jiang, 2022. "Statistical Inference of Wiener Constant-Stress Accelerated Degradation Model with Random Effects," Mathematics, MDPI, vol. 10(16), pages 1-18, August.

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