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Bootstrap analysis of designed experiments for reliability improvement with a non-constant scale parameter

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  • Wang, Guodong
  • He, Zhen
  • Xue, Li
  • Cui, Qingan
  • Lv, Shanshan
  • Zhou, Panpan

Abstract

Factors which significantly affect product reliability are of great interest to reliability practitioners. This paper proposes a bootstrap-based methodology for identifying significant factors when both location and scale parameters of the smallest extreme value distribution vary over experimental factors. An industrial thermostat experiment is presented, analyzed, and discussed as an illustrative example. The analysis results show that 1) the misspecification of a constant scale parameter may lead to misidentify spurious effects; 2) the important factors identified by different bootstrap methods (i.e., percentile bootstrapping, bias-corrected percentile bootstrapping, and bias-corrected and accelerated percentile bootstrapping) are different; 3) the number of factors affecting 10th percentile lifetime significantly is less than the number of important factors identified at 63.21th percentile.

Suggested Citation

  • Wang, Guodong & He, Zhen & Xue, Li & Cui, Qingan & Lv, Shanshan & Zhou, Panpan, 2017. "Bootstrap analysis of designed experiments for reliability improvement with a non-constant scale parameter," Reliability Engineering and System Safety, Elsevier, vol. 160(C), pages 114-121.
    • Handle: RePEc:eee:reensy:v:160:y:2017:i:c:p:114-121
    DOI: 10.1016/j.ress.2016.12.006
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    References listed on IDEAS

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    1. Piña-Monarrez, Manuel R. & Ortiz-Yañez, Jesús F., 2015. "Weibull and lognormal Taguchi analysis using multiple linear regression," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 244-253.
    2. Chou, Chao-Yu & Lin, Yu-Chang & Chang, Chun-Lang & Chen, Chung-Ho, 2006. "On the bootstrap confidence intervals of the process incapability index Cpp," Reliability Engineering and System Safety, Elsevier, vol. 91(4), pages 452-459.
    3. Seo, J.H. & Jung, M. & Kim, C.M., 2009. "Design of accelerated life test sampling plans with a nonconstant shape parameter," European Journal of Operational Research, Elsevier, vol. 197(2), pages 659-666, September.
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    Cited by:

    1. Wang, Guodong & Shao, Mengying & Lv, Shanshan & Kong, Xiangfen & He, Zhen & Vining, Geoff, 2022. "Process parameter optimization for lifetime improvement experiments considering warranty and customer satisfaction," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Zhuang, Liangliang & Xu, Ancha & Pang, Jihong, 2021. "Product reliability analysis based on heavily censored interval data with batch effects," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    3. Liu, Fuchao & Wei, Pengfei & Tang, Chenghu & Wang, Pan & Yue, Zhufeng, 2019. "Global sensitivity analysis for multivariate outputs based on multiple response Gaussian process model," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 287-298.

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