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Reliability estimation from two types of accelerated testing data based on an artificial neural network supported Wiener process

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  • Liu, Di
  • Wang, Shaoping
  • Zhang, Chao

Abstract

Considering the powerful ability of artificial neural network on data fitting, we have combined it with Wiener process in reliability estimation, while the previously published artificial neural network supported Wiener process based reliability estimation method only can utilize degradation testing data under normal use stress. However, the accelerated degradation tests and accelerated life tests are always performed to shorten the testing time. Hence, based on a general log-linear form acceleration model, we develop the artificial neural network supported Wiener process model into an accelerated testing model to utilize the accelerated testing data and improve the reliability estimating accuracy. Furthermore, the corresponding model training and parameter inferencing approaches are also constructed to apply the proposed model in reliability estimation. A simulation experiment and a case study on stress relaxation dataset are performed to verify the effectiveness of the proposed method. It is concluded that, the proposed method shows superiorities on population evaluation and individual predictions comparing the other methods, considering the accuracies of degradation modeling, life and reliability estimations. As a result, it is more suitable and practical than the previously published approaches.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:417:y:2022:i:c:s0096300321008390
    DOI: 10.1016/j.amc.2021.126757
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    References listed on IDEAS

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    Cited by:

    1. Xu, Qinqin & Zhu, Yuanguo, 2023. "Reliability analysis of uncertain random systems based on uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    2. Zhang, Yadong & Zhang, Chao & Wang, Shaoping & Dui, Hongyan & Chen, Rentong, 2024. "Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    3. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2022. "Minimizing mission cost for production system with unreliable storage," Reliability Engineering and System Safety, Elsevier, vol. 227(C).

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