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Nonlinear degradation model and reliability analysis by integrating image covariate

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  • Chen, Xingyu
  • Yang, Qingyu
  • Wu, Xin

Abstract

Degradation-based reliability analysis is an essential method for reliability prediction and prognosis of critical systems. Recently, with the advances of information technology, high-dimensional data such as images are available to improve system modeling and analysis. In this research, we propose a new nonlinear degradation model that integrates material microstructure image covariates. Based on the proposed model, product reliability can be precisely predicted and the failure time distribution is calculated. A maximum likelihood estimation method and an expectation-maximization method are developed to estimate the model parameters. Simulation studies are conducted to exam the effectiveness of the developed model and the model parameter estimation method. In the case study, the developed methodology is applied to a real-world problem of dual-phase advanced high strength steel (AHSS), which is now widely used in the automotive and aerospace industries. The results show that the proposed model can effectively model the nonlinear degradation trend with material image covariates, and the model greatly outperforms multiple existing methods.

Suggested Citation

  • Chen, Xingyu & Yang, Qingyu & Wu, Xin, 2022. "Nonlinear degradation model and reliability analysis by integrating image covariate," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:reensy:v:225:y:2022:i:c:s0951832022002472
    DOI: 10.1016/j.ress.2022.108602
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    1. 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).
    2. 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).
    3. Nailong Zhang & Qingyu Yang, 2016. "A random effect autologistic regression model with application to the characterization of multiple microstructure samples," IISE Transactions, Taylor & Francis Journals, vol. 48(1), pages 34-42, January.
    4. Cao, Yudong & Ding, Yifei & Jia, Minping & Tian, Rushuai, 2021. "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. 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).
    6. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2020. "Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. 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).
    8. Jiang, R. & Jardine, A.K.S., 2008. "Health state evaluation of an item: A general framework and graphical representation," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 89-99.
    9. Wujun Si & Qingyu Yang & Xin Wu, 2016. "A physical–statistical model of overload retardation for crack propagation and application in reliability estimation," IISE Transactions, Taylor & Francis Journals, vol. 48(4), pages 347-358, April.
    10. Dong, Qinglai & Cui, Lirong, 2019. "A study on stochastic degradation process models under different types of failure Thresholds," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 202-212.
    11. Syamsundar, A. & Naikan, V.N.A. & Wu, Shaomin, 2021. "Extended Arithmetic Reduction of Age Models for the Failure Process of a Repairable System," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. Huang, Jianlin & Golubović, Dušan S & Koh, Sau & Yang, Daoguo & Li, Xiupeng & Fan, Xuejun & Zhang, G.Q., 2016. "Lumen degradation modeling of white-light LEDs in step stress accelerated degradation test," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 152-159.
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