Weibull and lognormal Taguchi analysis using multiple linear regression

被引:20
|
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] Short Term Load Forecasting Using ANN and Multiple Linear Regression
    Kumar, Sharad
    Mishra, Shashank
    Gupta, Shashank
    2016 SECOND INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE & COMMUNICATION TECHNOLOGY (CICT), 2016, : 184 - 186
  • [42] Predictive Big Data Analytics Using Multiple Linear Regression Model
    Khine, Kyi Lai Lai
    Nyunt, Thi Thi Soe
    BIG DATA ANALYSIS AND DEEP LEARNING APPLICATIONS, 2019, 744 : 9 - 19
  • [43] The general Box-Cox transformations in multiple linear regression analysis
    Li, BB
    De Moor, B
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2002, 31 (04) : 673 - 687
  • [44] Multiple linear regression analysis of relationship between business results and strategy
    Pasic, Mugdim
    Sunje, Aziz
    Bijelonja, Izet
    Kadric, Edin
    ANNALS OF DAAAM FOR 2007 & PROCEEDINGS OF THE 18TH INTERNATIONAL DAAAM SYMPOSIUM: INTELLIGENT MANUFACTURING & AUTOMATION: FOCUS ON CREATIVITY, RESPONSIBILITY, AND ETHICS OF ENGINEERS, 2007, : 549 - 550
  • [45] Development of Imputation Methods for Missing Data in Multiple Linear Regression Analysis
    Thidarat Thongsri
    Klairung Samart
    Lobachevskii Journal of Mathematics, 2022, 43 : 3390 - 3399
  • [46] Multiple linear regression analysis of vertical distribution of nearshore suspended sediment
    Zhu, Wenjin
    Yu, Wei
    Dong, Xiaotian
    Jin, Zigui
    Hu, Shunqiu
    DESALINATION AND WATER TREATMENT, 2023, 314 : 352 - 358
  • [47] Multiple linear regression analysis of vertical distribution of nearshore suspended sediment
    Zhu, Wenjin
    Yu, Wei
    Dong, Xiaotian
    Jin, Zigui
    Hu, Shunqiu
    DESALINATION AND WATER TREATMENT, 2023, 314 : 352 - 358
  • [48] Development of Imputation Methods for Missing Data in Multiple Linear Regression Analysis
    Thongsri, Thidarat
    Samart, Klairung
    LOBACHEVSKII JOURNAL OF MATHEMATICS, 2022, 43 (11) : 3390 - 3399
  • [49] The OWA operator in multiple linear regression
    Flores-Sosa M.
    Avilés-Ochoa E.
    Merigó J.M.
    Kacprzyk J.
    Applied Soft Computing, 2022, 124
  • [50] Ionospheric TEC forecasting using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey
    Samed Inyurt
    Mahsa Hasanpour Kashani
    Aliihsan Sekertekin
    Astrophysics and Space Science, 2020, 365