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Reliability assessment model considering heterogeneous population in a multiple stresses accelerated test

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  • Lin, Kunsong
  • Chen, Yunxia
  • Xu, Dan

Abstract

Heterogeneous population, a mixture of weak and strong subpopulations, is inevitable in some multiple stresses accelerated tests. Modeling the reliability of heterogeneous population in an accelerated test differs dramatically from that for a homogeneous setting. In this paper, a multiple stress reliability assessment model with heterogeneous populations is proposed, which includes verifying the presence of heterogeneous population, determining the number of subpopulations and separating populations based on Bayes classifier. The acceleration model structure is then specified, and the effects of different accelerating stresses are analyzed. A practical example is used to demonstrate the accuracy and flexibility of the proposed method. It is shown that reliability assessment without considering heterogeneity is heavily biased, and the sequences of stress sensitivity to different subpopulations are different. We also explain the phenomenon that the pseudo lifetime of smart electricity meter under some milder stress is shorter than that in harsher condition due to opposite effects on degradation characteristics among different stresses, and verify the phenomenon by the enhancement test.

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  • Lin, Kunsong & Chen, Yunxia & Xu, Dan, 2017. "Reliability assessment model considering heterogeneous population in a multiple stresses accelerated test," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 134-143.
    • Handle: RePEc:eee:reensy:v:165:y:2017:i:c:p:134-143
    DOI: 10.1016/j.ress.2017.03.013
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    6. Dan Xu & Jiaolan He & Zhou Yang, 2022. "Reliability prediction based on Birnbaum–Saunders model and its application to smart meter," Annals of Operations Research, Springer, vol. 312(1), pages 519-532, May.
    7. Ekene Gabriel Okafor & Whit Vinson & David Ryan Huitink, 2023. "Effect of Stress Interaction on Multi-Stress Accelerated Life Test Plan: Assessment Based on Particle Swarm Optimization," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    8. Han, David & Bai, Tianyu, 2020. "Design optimization of a simple step-stress accelerated life test – Contrast between continuous and interval inspections with non-uniform step durations," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    10. Ye, Xuerong & Hu, Yifan & Zheng, Bokai & Chen, Cen & Zhai, Guofu, 2022. "A new class of multi-stress acceleration models with interaction effects and its extension to accelerated degradation modelling," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Woo, Seong-woo & Pecht, Michael & O'Neal, Dennis L., 2020. "Reliability design and case study of the domestic compressor subjected to repetitive internal stresses," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    12. 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).
    13. Le Liu & Xiao-Yang Li & Enrico Zio & Rui Kang & Tong-Min Jiang, 2017. "Model Uncertainty in Accelerated Degradation Testing Analysis," Post-Print hal-01652218, HAL.
    14. Hunjra, Ahmed Imran & Azam, Muhammad & Bruna, Maria Giuseppina & Verhoeven, Peter & Al-Faryan, Mamdouh Abdulaziz Saleh, 2022. "Sustainable development: The impact of political risk, macroeconomic policy uncertainty and ethnic conflict," International Review of Financial Analysis, Elsevier, vol. 84(C).
    15. Moustafa, Kassem & Hu, Zhen & Mourelatos, Zissimos P. & Baseski, Igor & Majcher, Monica, 2021. "System reliability analysis using component-level and system-level accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    16. 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).
    17. Yao Liu & Yashun Wang & Zhengwei Fan & Xun Chen & Chunhua Zhang & Yuanyuan Tan, 2020. "A new universal multi-stress acceleration model and multi-parameter estimation method based on particle swarm optimization," Journal of Risk and Reliability, , vol. 234(6), pages 764-778, December.

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