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An artificial neural network supported Wiener process based reliability estimation method considering individual difference and measurement error

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  • Liu, Di
  • Wang, Shaoping
  • Cui, Xiaoyu

Abstract

Due to the powerful ability of artificial neural network in data fitting, it has been applied to describe the mean function in Wiener process for degradation modeling and estimating reliability. However, the previously published method neglects the measurement error and individual difference, which need to be considered in reliability estimation. In order to handle the above issues, the artificial neural network supported Wiener process is improved. Both the individual difference and measurement error are considered. The measurement error is described by a normal distribution with zero mean. The individual difference is described by introducing hyper parameters. In order to demonstrate the proposed method, a simulation study and a case study have been performed. It can be seen that, considering the measurement error and individual difference can improve the reliability and lifetime estimation accuracies, resulting in the proposed method is more powerful and more practical in engineering practice.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pb:s0951832021006499
    DOI: 10.1016/j.ress.2021.108162
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    References listed on IDEAS

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    1. Wang, Lizhi & Pan, Rong & Li, Xiaoyang & Jiang, Tongmin, 2013. "A Bayesian reliability evaluation method with integrated accelerated degradation testing and field information," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 38-47.
    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. Lanqing Hong & Zhi-Sheng Ye & Josephine Kartika Sari, 2018. "Interval estimation for Wiener processes based on accelerated degradation test data," IISE Transactions, Taylor & Francis Journals, vol. 50(12), pages 1043-1057, December.
    4. 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.
    5. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    6. 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.
    7. 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.
    8. Wang, Xiao, 2010. "Wiener processes with random effects for degradation data," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 340-351, February.
    9. 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).
    10. 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.
    11. Nguyen, Khanh T.P. & Fouladirad, Mitra & Grall, Antoine, 2018. "Model selection for degradation modeling and prognosis with health monitoring data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 105-116.
    12. 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).
    13. 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).
    14. Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.
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