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Reliability estimation from two types of accelerated testing data considering measurement error

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  • Ma, Zhonghai
  • Wang, Shaoping
  • Ruiz, Cesar
  • Zhang, Chao
  • Liao, Haitao
  • Pohl, Edward

Abstract

Reliability testing is an indispensable tool for evaluating the lifetime of a product. However, for a highly reliable product, it is quite common that a large proportion of test units will be censored in a regular life test or even in accelerated life testing (ALT) when the total testing time is too short. As an alternative, accelerated degradation testing (ADT) can be conducted to collect degradation data of a highly reliable product under accelerated conditions. For a reliability practitioner, it will be very valuable to use both ALT and ADT data for reliability estimation. In practice, degradation data are often contaminated by measurement error, which may affect the accuracy of reliability estimation. Therefore, a statistical procedure is needed when using both ALT data and ADT data with measurement error for evaluating the reliability of a highly reliable product. In this paper, an Inverse Gaussian (IG) process is used to model the degradation process of a product considering measurement error. To incorporate the two types of accelerated testing data, a new expectation-maximization (EM) algorithm is developed to estimate the model parameters by taking advantage of the parameter structure. A simulation study and a case study on a hydraulic piston pump are presented to illustrate the practical value of the proposed method in improving the accuracy of reliability estimation for a highly reliable product.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:193:y:2020:i:c:s0951832019304399
    DOI: 10.1016/j.ress.2019.106610
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    References listed on IDEAS

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    1. Heonsang Lim & Bong-Jin Yum, 2011. "Optimal design of accelerated degradation tests based on Wiener process models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 309-325, September.
    2. 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.
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    5. Peng, Weiwen & Li, Yan-Feng & Yang, Yuan-Jian & Huang, Hong-Zhong & Zuo, Ming J., 2014. "Inverse Gaussian process models for degradation analysis: A Bayesian perspective," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 175-189.
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    Citations

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    Cited by:

    1. Liu, Yao & Wang, Yashun & Fan, Zhengwei & Bai, Guanghan & Chen, Xun, 2021. "Reliability modeling and a statistical inference method of accelerated degradation testing with multiple stresses and dependent competing failure processes," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Geng, Yixuan & Wang, Shaoping & Shi, Jian & Zhang, Yuwei & Wang, Weijie, 2023. "Reliability modeling of phased degradation under external shocks," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. Ma, Zhonghai & Liao, Haitao & Ji, Hui & Wang, Shaoping & Yin, Fanglong & Nie, Songlin, 2021. "Optimal design of hybrid accelerated test based on the Inverse Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 210(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. 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).
    6. Liu, Di & Wang, Shaoping, 2021. "An artificial neural network supported stochastic process for degradation modeling and prediction," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    7. Md. Abu Ayub Siddique & Yong-Joo Kim & Seung-Min Baek & Seung-Yun Baek & Tae-Ho Han & Wan-Soo Kim & Yeon-Soo Kim & Ryu-Gap Lim & Yong Choi, 2022. "Development of the Reliability Assessment Process of the Hydraulic Pump for a 78 kW Tractor during Major Agricultural Operations," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
    8. 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).
    9. 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).

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