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Reliability estimation from lifetime testing data and degradation testing data with measurement error based on evidential variable and Wiener process

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  • Liu, Di
  • Wang, Shaoping

Abstract

Evidential variable has been applied in Wiener process based reliability estimation due to its powerful ability on parameter describing. The previously published evidential variable and stochastic process based reliability estimation methods neglect measurement error and cannot utilize lifetime testing data. However, in practical applications, lifetime testing is an important approach and measurement error is an inevitable factor. Hence, in this paper, the evidential variable and Wiener process based reliability estimation method is improved to handle the above issues. A simulation study is used to verify the effectiveness of the proposed reliability estimation method. Furthermore, an actual engineering case on piston pump is also studied to demonstrate the proposed method in engineering practice. It is concluded that utilizing lifetime testing data and considering measurement error can improve the accuracies of model parameter evaluation, degradation prediction, reliability estimation and etc., in evidential and Wiener process based reliability estimation.

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  • 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).
    • Handle: RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307316
    DOI: 10.1016/j.ress.2020.107231
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    References listed on IDEAS

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    2. Jiaxin Yang & Shengjin Tang & Pengya Fang & Fengfei Wang & Xiaoyan Sun & Xiaosheng Si, 2024. "Remaining useful life prediction of implicit linear Wiener degradation process based on multi-source information," Journal of Risk and Reliability, , vol. 238(1), pages 93-111, February.
    3. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(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. Cheng, Yao & Liao, Haitao & Huang, Zhiyi, 2021. "Optimal degradation-based hybrid double-stage acceptance sampling plan for a heterogeneous product," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    7. Zheng, Huiling & Kong, Xuefeng & Xu, Houbao & Yang, Jun, 2021. "Reliability analysis of products based on proportional hazard model with degradation trend and environmental factor," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    8. Shuto, Susumu & Amemiya, Takashi, 2022. "Sequential Bayesian inference for Weibull distribution parameters with initial hyperparameter optimization for system reliability estimation," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    9. Xu, Xiaodong & Tang, Shengjin & Yu, Chuanqiang & Xie, Jian & Han, Xuebing & Ouyang, Minggao, 2021. "Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Jia, Xiang & Guo, Bo, 2022. "Reliability analysis for complex system with multi-source data integration and multi-level data transmission," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    11. He, Jiabei & Tian, Yi & Wu, Lifeng, 2022. "A hybrid data-driven method for rapid prediction of lithium-ion battery capacity," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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