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Relation between aging intensity function and WPP plot and its application in reliability modelling

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  • Jiang, Renyan
  • Qi, Faqun
  • Cao, Yu

Abstract

Parameter estimation on heavily censored data is a challenging problem, and new methods are needed. This paper aims to address this issue. It is based on a relation between the Weibull probability paper (WPP) plot and the aging intensity function (AIF): the slope of tangent line of the WPP plot is equal to the AIF. Based on this property, a WPP-based local regression method is developed to obtain the empirical AIF, which can aid model selection; and a WPP-based global regression method is proposed to obtain a sample of the shape parameter of a distribution, from which the shape parameter is estimated. Once this is done, the point estimate of the shape parameter is fixed and a single-parameter maximum likelihood method is used to estimate the scale parameter. The proposed method is applicable for both the Weibull and non-Weibull distributions. Two examples are included to illustrate the proposed method and its appropriateness. The results show that the proposed method outperforms the existing methods in terms of applicability, simplicity, accuracy, robustness and unbiasness.

Suggested Citation

  • Jiang, Renyan & Qi, Faqun & Cao, Yu, 2023. "Relation between aging intensity function and WPP plot and its application in reliability modelling," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004999
    DOI: 10.1016/j.ress.2022.108894
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    1. Rifaai, Talha M. & Abokifa, Ahmed A. & Sela, Lina, 2022. "Integrated approach for pipe failure prediction and condition scoring in water infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 220(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. Gámiz, Maria Luz & Nozal-Cañadas, Rafael & Raya-Miranda, Rocío, 2020. "TTT-SiZer: A graphic tool for aging trends recognition," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    4. Ranjan, Rakesh & Sen, Rijji & Upadhyay, Satyanshu K., 2021. "Bayes analysis of some important lifetime models using MCMC based approaches when the observations are left truncated and right censored," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    5. Starling, James K. & Mastrangelo, Christina & Choe, Youngjun, 2021. "Improving Weibull distribution estimation for generalized Type I censored data using modified SMOTE," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    6. 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).
    7. Magdalena Szymkowiak & Maria Iwińska, 2019. "Some results about bivariate discrete distributions through the vector of aging intensities," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(9), pages 2175-2184, May.
    8. Jiang, R., 2014. "A drawback and an improvement of the classical Weibull probability plot," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 135-142.
    9. Renyan Jiang, 2015. "Introduction to Quality and Reliability Engineering," Springer Series in Reliability Engineering, Springer, edition 127, number 978-3-662-47215-6, September.
    10. Chehade, Abdallah & Savargaonkar, Mayuresh & Krivtsov, Vasiliy, 2022. "Conditional Gaussian mixture model for warranty claims forecasting," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    11. Zhang, Cai Wen, 2021. "Weibull parameter estimation and reliability analysis with zero-failure data from high-quality products," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    12. Szymkowiak, Magdalena, 2018. "Generalized aging intensity functions," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 198-208.
    13. Caroni, C., 2010. "“Failure limited†data and TTT-based trend tests in multiple repairable systems," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 704-706.
    14. Elmahdy, Emad E., 2015. "A new approach for Weibull modeling for reliability life data analysis," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 708-720.
    15. Zhu, Tiefeng, 2020. "Reliability estimation for two-parameter Weibull distribution under block censoring," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    16. Acitas, Sukru & Aladag, Cagdas Hakan & Senoglu, Birdal, 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 and System Safety, Elsevier, vol. 183(C), pages 116-127.
    17. Hooti, Fatemeh & Ahmadi, Jafar & Longobardi, Maria, 2020. "Optimal extended warranty length with limited number of repairs in the warranty period," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    18. Peña-Ramírez, Fernando A. & Guerra, Renata Rojas & Canterle, Diego Ramos & Cordeiro, Gauss M., 2020. "The logistic Nadarajah–Haghighi distribution and its associated regression model for reliability applications," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    Full references (including those not matched with items on IDEAS)

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