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Reliability modeling and evaluation of lifetime delayed degradation process with nondestructive testing

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  • Li, Zan
  • Wang, Fengming
  • Wang, Chengjie
  • Hu, Qingpei
  • Yu, Dan

Abstract

The sequential hard and soft failure mode, a typical failure phenomenon, involves degradation that starts after an initiation period. According to this situation, a random initiation effect is introduced to the normal degradation stage. Intuitively, a lifetime delayed degradation process (LDDP) provides a general framework for this typical complex failure mode. In the present study, general reliability inference approaches involving the joint likelihood function are developed for the LDDP with repeated measurements, according to the expectation maximization (EM) and stochastic EM algorithms, along with numerical simulations and practical application based on real data. Additionally, statistical inferences were obtained using a bootstrap method on the basis of parameter estimations. The proposed method was compared with the traditional two-step strategy under different sample sizes and inspection frequencies and exhibited enhanced performance.

Suggested Citation

  • Li, Zan & Wang, Fengming & Wang, Chengjie & Hu, Qingpei & Yu, Dan, 2021. "Reliability modeling and evaluation of lifetime delayed degradation process with nondestructive testing," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:reensy:v:208:y:2021:i:c:s0951832020308486
    DOI: 10.1016/j.ress.2020.107358
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. Yang, Xiuzhen & He, Yihai & Liao, Ruoyu & Cai, Yuqi & Ai, Jun, 2022. "Integrated mission reliability modeling based on extended quality state task network for intelligent multistate manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    4. Wang, Han & Liao, Haitao & Ma, Xiaobing, 2022. "Stochastic Multi-phase Modeling and Health Assessment for Systems Based on Degradation Branching Processes," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    5. Yan, Weian & Xu, Xiaofan & Bigaud, David & Cao, Wenqin, 2023. "Optimal design of step-stress accelerated degradation tests based on the Tweedie exponential dispersion process," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Wang, Wenzhuo & He, Yihai & Liao, Ruoyu & Cai, Yuqi & Zheng, Xin & Zhao, Yu, 2022. "Mission reliability driven functional healthy state modeling approach considering production rhythm and workpiece quality for manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. 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).
    8. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Wang, Chao & Zhu, Tao & Yang, Bing & Yin, Minxuan & Xiao, Shoune & Yang, Guangwu, 2023. "Remaining useful life prediction framework for crack propagation with a case study of railway heavy duty coupler condition monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    10. 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).
    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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