Bayesian analysis of accelerated life test under constrained randomization

References

• Acitas, S., C. H. Aladag, and B. Senoglu. 2019. A new approach for estimating the parameters of Weibull distribution via particle swarm optimization: An application to the strengths of glass fibre data. Reliability Engineering & System Safety 183:116–27. doi: 10.1016/j.ress.2018.07.024.
   Web of Science ®Google Scholar
• Antony, J. 2000. Improving the manufacturing process quality and capability using experimental design: A case study. International Journal of Production Research 38 (12):2607–18. doi: 10.1080/002075400411385.
   Web of Science ®Google Scholar
• Bagdonavicius, V., and M. Nikulin. 2001. Accelerated life models: Modeling and statistical analysis. New York: Chapman and Hall/CRC.
   Google Scholar
• Balakrishnan, N., and M. H. Ling. 2013. Expectation maximization algorithm for one shot device accelerated life testing with Weibull lifetimes, and variable parameters over stress. IEEE Transactions on Reliability 62 (2):537–51. doi: 10.1109/TR.2013.2257054.
   Web of Science ®Google Scholar
• Chen, D. G., Y. L. Lio, and N. Jiang. 2013. Lower confidence limits on the generalized exponential distribution percentiles under progressive type-I interval censoring. Communications in Statistics - Simulation and Computation 42 (9):2106–17. doi: 10.1080/03610918.2012.695842.
   Web of Science ®Google Scholar
• Condra, L. 2001. Reliability improvement with design of experiment. New York, NY: CRC Press. doi: 10.1201/9781482270846.
   Google Scholar
• Ding, X., H. Xu, C. Hopper, J. Yang, and C. M. Ho. 2013. Use of fractional factorial designs in antiviral drug studies. Quality and Reliability Engineering International 29 (2):299–304. doi: 10.1002/qre.1308.
   Web of Science ®Google Scholar
• Duan, F., and G. Wang. 2022. Bayesian analysis for the transformed exponential dispersion process with random effects. Reliability Engineering & System Safety 217:108104. doi: 10.1016/j.ress.2021.108104.
   Web of Science ®Google Scholar
• Edwards, D. J., F. M. Guess, R. V. León, T. M. Young, and K. A. Crookston. 2014. Improving estimates of critical lower percentiles by induced censoring. Reliability Engineering & System Safety 123:47–56. doi: 10.1016/j.ress.2013.10.001.
   Web of Science ®Google Scholar
• Edwards, D. J., F. M. Guess, and T. M. Young. 2011. Improved estimation of the lower percentiles of material properties. Wood Science and Technology 45 (3):533–46. doi: 10.1007/s00226-010-0346-2.
   Web of Science ®Google Scholar
• Fang, G., R. Pan, and Y. Wang. 2022. Inverse Gaussian processes with correlated random effects for multivariate degradation modeling. European Journal of Operational Research 300 (3):1177–93. doi: 10.1016/j.ejor.2021.10.049.
   Web of Science ®Google Scholar
• Feiveson, A. H., and P. M. Kulkarni. 2000. Reliability of space-shuttle pressure vessels with random batch effects. Technometrics 42 (4):332–44. doi: 10.1080/00401706.2000.10485706.
   Web of Science ®Google Scholar
• Freeman, L. J. 2010. Statistical methods for reliability data from designed experiments. Ph. D. dissertation, Virginia Polytechnic Institute and State University, Blacksburg.
   Google Scholar
• Freeman, L. J., and G. G. Vining. 2010. Reliability data analysis for life test experiments with subsampling. Journal of Quality Technology 42 (3):233–41. doi: 10.1080/00224065.2010.11917821.
   Web of Science ®Google Scholar
• Freeman, L. J., and G. G. Vining. 2013. Reliability data analysis for life test designed experiments with sub‐sampling. Quality and Reliability Engineering International 29 (4):509–19. doi: 10.1002/qre.1398.
   Web of Science ®Google Scholar
• Gamerman, D., and H. F. Lopes. 2006. Markov Chain Monte Carlo: Stochastic simulation for bayesian inference. 2nd ed. New York: Chapman and Hall/CRC. doi: 10.1201/9781482296426.
   Google Scholar
• Gerstle, F. P., and S. C. Kunz. 1983. Prediction of long-term failure in Kevlar 49 composites. In Long-term behavior of composites, ed. by T. K. O’Brian, 263–92. Philadelphia: ASTM STP 813.
   Google Scholar
• Guo, J., Y. F. Li, W. Peng, and H. Z. Huang. 2022. Bayesian information fusion method for reliability analysis with failure‐time data and degradation data. Quality and Reliability Engineering International 38 (4):1944–56. doi: 10.1002/qre.3065.
   Web of Science ®Google Scholar
• Hamada, M. S., A. G. Wilson, C. S. Reese, and H. F. Martz. 2008. Bayesian reliability. New York: Springer.
   Google Scholar
• Karras, A., C. Karras, N. Schizas, M. Avlonitis, and S. Sioutas. 2023. AutoML with Bayesian optimizations for big data management. Information 14 (4):223. doi: 10.3390/info14040223.
   Google Scholar
• Kensler, J. L., L. J. Freeman, and G. G. Vining. 2014. A practitioner’s guide to analyzing reliability experiments with random blocks and subsampling. Quality Engineering 26 (3):359–69. doi: 10.1080/08982112.2014.887101.
   Web of Science ®Google Scholar
• Kundu, D., and D. Mitra. 2016. Bayesian inference of Weibull distribution based on left truncated and right censored data. Computational Statistics & Data Analysis 99:38–50. doi: 10.1016/j.csda.2016.01.001.
   Web of Science ®Google Scholar
• Lawless, J. F. 2003. Statistical models and methods for lifetime data. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• León, R. V., Y. Li, F. M. Guess, and R. S. Sawhney. 2009. Effect of not having homogeneous test units in accelerated life tests. Journal of Quality Technology 41 (3):241–6. doi: 10.1080/00224065.2009.11917779.
   Web of Science ®Google Scholar
• León, R. V., R. Ramachandran, A. J. Ashby, and J. Thyagarajan. 2007. Bayesian modeling of accelerated life tests with random effects. Journal of Quality Technology 39 (1):3–16. doi: 10.1080/00224065.2007.11917669.
   Web of Science ®Google Scholar
• Li, M., and W. Q. Meeker. 2014. Application of Bayesian methods in reliability data analyses. Journal of Quality Technology 46 (1):1–23. doi: 10.1080/00224065.2014.11917951.
   Web of Science ®Google Scholar
• Lio, Y. L., J. C. Lu, and L. Ruan. 2015. Robust parameter design for quality and reliability issues based on accelerated degradation measurements. IEEE Transactions on Reliability 64 (3):949–59. doi: 10.1109/TR.2015.2415892.
   Web of Science ®Google Scholar
• Lv, S., Z. Niu, L. Qu, S. He, and Z. He. 2015. Reliability modeling of accelerated life tests with both random effects and nonconstant shape parameters. Quality Engineering 27 (3):329–40. doi: 10.1080/08982112.2015.1037393.
   Web of Science ®Google Scholar
• Lv, S., Z. Niu, G. Wang, L. Qu, and Z. He. 2017. Lower percentile estimation of accelerated life tests with nonconstant scale parameter. Quality and Reliability Engineering International 33 (7):1437–46. doi: 10.1002/qre.2116.
   Web of Science ®Google Scholar
• Medlin, R. M., L. J. Freeman, J. L. Kensler, and G. G. Vining. 2019. Analysis of split‐plot reliability experiments with subsampling. Quality and Reliability Engineering International 35 (3):738–49. doi: 10.1002/qre.2394.
   Web of Science ®Google Scholar
• Meeker, W. Q., and L. A. Escobar. 1993. A review of recent research and current issues in accelerated testing. International Statistical Review / Revue Internationale de Statistique 61 (1):147–68. doi: 10.2307/1403600.
   Web of Science ®Google Scholar
• Meeker, W. Q., and L. A. Escobar. 1998. Statistical methods for reliability data. New York: John Wiley & Sons.
   Google Scholar
• Meeter, C. A., and W. Q. Meeker. 1994. Optimum accelerated life tests wth a nonconstant scale parameter. Technometrics 36 (1):71–83. doi: 10.1080/00401706.1994.10485402.
   Web of Science ®Google Scholar
• Montgomery, D. C. 2017. Design and analysis of experiments. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Nelson, W. B. 2004. Accelerated testing: Statistical models, test plans, and data analysis. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Olwell, D. H., and A. A. Sorell. 2001. Warranty calculations for missiles with only current-status data, using Bayesian methods. In Annual Reliability and Maintainability Symposium. 2001 Proceedings. International Symposium on Product Quality and Integrity (Cat. No. 01CH37179), 133-138. doi: 10.1109/RAMS.2001.902455.
   Google Scholar
• Sirvanci, M. B., and M. Durmaz. 1993. Variation reduction by the use of designed experiments. Quality Engineering 5 (4):611–8. doi: 10.1080/08982119308919003.
   Google Scholar
• Szymkowiak, M. 2018. Characterizations of distributions through aging intensity. IEEE Transactions on Reliability 67 (2):446–58. doi: 10.1109/TR.2018.2817739.
   Web of Science ®Google Scholar
• Viertl, R. 1988. Statistical methods in accelerated life testing. Göttingen: Vandenhoeck and Ruprecht.
   Google Scholar
• Vining, G. G., L. J. Freeman, and J. L. Kensler. 2015. An overview of designing experiments for reliability data. Frontiers in Statistical Quality Control 11:321–36.
   Google Scholar
• Wang, B. X. 2004. Parameter estimation of constant acceleration test with non-constant shape parameters for Weibull distribution. Journal of Applied Mathematics 27 (1):44–51.
   Google Scholar
• Wang, F. K., B. Bizuneh, and X. B. Cheng. 2018. New control charts for monitoring the Weibull percentiles under complete data and Type‐II censoring. Quality and Reliability Engineering International 34 (3):403–16. doi: 10.1002/qre.2261.
   Web of Science ®Google Scholar
• Wang, G., M. Shao, J. Li, S. Lv, X. Kong, and Y. Zhou. 2022. Bayesian analysis of designed reliability improvement experiments with application to adaptive termination. Quality and Reliability Engineering International. doi: 10.1002/qre.3226.
   PubMed Web of Science ®Google Scholar
• Wang, G., Z. Niu, and Z. He. 2015. Bias reduction of MLEs for Weibull distributions under grouped lifetime data. Quality Engineering 27 (3):341–52. doi: 10.1080/08982112.2015.1037394.
   Web of Science ®Google Scholar
• Wang, J., G. Yang, and Z. Feng. 2020. Bayesian modeling ang analysis of accelerated life data. Systems Engineering and Electronics 43 (5):1420–9.
   Google Scholar
• Wang, L. 2018. Estimation of constant-stress accelerated life test for Weibull distribution with nonconstant shape parameter. Journal of Computational and Applied Mathematics 343:539–55. doi: 10.1016/j.cam.2018.05.012.
   Web of Science ®Google Scholar
• Wang, L., C. Zhang, Y. M. Tripathi, S. Dey, and S. J. Wu. 2021. Reliability analysis of Weibull multicomponent system with stress‐dependent parameters from accelerated life data. Quality and Reliability Engineering International 37 (6):2603–21. doi: 10.1002/qre.2878.
   Web of Science ®Google Scholar
• Yan, Z., T. Zhu, X. Peng, and X. Li. 2018. Reliability analysis for multi-level stress testing with Weibull regression model under the general progressively Type-II censored data. Journal of Computational and Applied Mathematics 330:28–40. doi: 10.1016/j.cam.2017.05.048.
   Web of Science ®Google Scholar
• Yuan, R., M. Tang, H. Wang, and H. Li. 2019. A reliability analysis method of accelerated performance degradation based on Bayesian strategy. IEEE Access. 7:169047–54. doi: 10.1109/ACCESS.2019.2952337.
   Web of Science ®Google Scholar
• Zelen, M. 1959. Factorial experiments in life testing. Technometrics 1 (3):269–88. doi: 10.1080/00401706.1959.10489862.
   Google Scholar
• Zhai, Q., P. Chen, L. Hong, and L. Shen. 2018. A random-effects Wiener degradation model based on accelerated failure time. Reliability Engineering & System Safety 180:94–103. doi: 10.1016/j.ress.2018.07.003.
   Web of Science ®Google Scholar
• Zhang, S., and Y. Zhang. 2021. Nonlinear mixed reliability model with non-constant shape parameter of aviation cables. Applied Mathematical Modelling 96:445–55. doi: 10.1016/j.apm.2021.03.011.
   Web of Science ®Google Scholar
• Zhang, C. W., R. Pan, and T. N. Goh. 2021. Reliability assessment of high-quality new products with data scarcity. International Journal of Production Research 59 (14):4175–87. doi: 10.1080/00207543.2020.1758355.
   Web of Science ®Google Scholar
• Zhu, T. 2020. Reliability estimation for two-parameter Weibull distribution under block censoring. Reliability Engineering & System Safety 203:107071. doi: 10.1016/j.ress.2020.107071.
   Web of Science ®Google Scholar