Weibull and lognormal Taguchi analysis using multiple linear regression

被引:19
|
作者
Pina-Monarrez, Manuel R. [1 ]
Ortiz-Yanez, Jesus F. [1 ]
机构
[1] Univ Autonoma Ciudad Juarez, Ind & Mfg Dept, Engn & Technol Inst, Cd Juarez 32310, Chih, Mexico
关键词
Taguchi method; Weibull analysis; Accelerated life testing analysis; Multiple linear regression; RELIABILITY;
D O I
10.1016/j.ress.2015.08.004
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The paper provides to reliability practitioners with a method (I) to estimate the robust Weibull family when the Taguchi method (TM) is applied, (2) to estimate the normal operational Weibull family in an accelerated life testing (ALT) analysis to give confidence to the extrapolation and (3) to perform the ANOVA analysis to both the robust and the normal operational Weibull family. On the other hand, because the Weibull distribution neither has the normal additive property nor has a direct relationship with the normal parameters (mu, sigma), in this paper, the issues of estimating a Weibull family by using a design of experiment (DOE) are first addressed by using an L-9 (3(4)) orthogonal array (OA) in both the TM and in the Weibull proportional hazard model approach (WPHM). Then, by using the Weibull/Gumbel and the lognormal/normal relationships and multiple linear regression, the direct relationships between the Weibull and the lifetime parameters are derived and used to formulate the proposed method. Moreover, since the derived direct relationships always hold, the method is generalized to the lognormal and ALT analysis. Finally, the method's efficiency is shown through its application to the used OA and to a set of ALT data. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:244 / 253
页数:10
相关论文
共 50 条
  • [41] ESTIMATING WEIBULL PARAMETERS BY LINEAR AND NONLINEAR-REGRESSION
    BERGER, RW
    LAWRENCE, K
    [J]. TECHNOMETRICS, 1974, 16 (04) : 617 - 619
  • [42] Unbiased estimation of Weibull parameters with the linear regression method
    Wu, DF
    Zhou, JC
    Li, YD
    [J]. JOURNAL OF THE EUROPEAN CERAMIC SOCIETY, 2006, 26 (07) : 1099 - 1105
  • [43] Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients
    HazimMansoorGorgees
    Mahdi, FatimahAssim
    [J]. IBN AL-HAITHAM FIRST INTERNATIONAL SCIENTIFIC CONFERENCE, 2018, 1003
  • [44] Analysis of bioenergy by using linear regression
    Muhammad Shahid Iqbal
    Tamoor Khan
    Samina Kausar
    Luo Bin
    [J]. SN Applied Sciences, 2019, 1
  • [45] Analysis of bioenergy by using linear regression
    Iqbal, Muhammad Shahid
    Khan, Tamoor
    Kausar, Samina
    Bin, Luo
    [J]. SN APPLIED SCIENCES, 2019, 1 (10)
  • [47] Regression: multiple linear
    Bangdiwala, Shrikant I.
    [J]. INTERNATIONAL JOURNAL OF INJURY CONTROL AND SAFETY PROMOTION, 2018, 25 (02) : 232 - 236
  • [48] Multiple linear regression
    Martin Krzywinski
    Naomi Altman
    [J]. Nature Methods, 2015, 12 : 1103 - 1104
  • [49] Multiple linear regression
    Krzywinski, Martin
    Altman, Naomi
    [J]. NATURE METHODS, 2015, 12 (12) : 1103 - 1104
  • [50] MULTIPLE LINEAR REGRESSION
    Gregoire, G.
    [J]. STATISTICS FOR ASTROPHYSICS: METHODS AND APPLICATIONS OF THE REGRESSION, 2015, 66 : 45 - 72