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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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    2. Wang, Jian & Gao, Shibin & Yu, Long & Ma, Chaoqun & Zhang, Dongkai & Kou, Lei, 2023. "A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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