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Choosing an optimal model for failure data analysis by graphical approach

Journal Article


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Abstract


  • Many models involving combination of multiple Weibull distributions, modification of Weibull distribution or extension of its modified ones, etc. have been developed to model a given set of failure data. The application of these models to modeling a given data set can be based on plotting the data on Weibull probability paper (WPP). Of them, two or more models are appropriate to model one typical shape of the fitting plot, whereas a specific model may be fit for analyzing different shapes of the plots. Hence, a problem arises, that is how to choose an optimal model for a given data set and how to model the data. The motivation of this paper is to address this issue. This paper summarizes the characteristics of Weibull-related models with more than three parameters including sectional models involving two or three Weibull distributions, competing risk model and mixed Weibull model. The models as discussed in this present paper are appropriate to model the data of which the shapes of plots on WPP can be concave, convex, S-shaped or inversely S-shaped. Then, the method for model selection is proposed, which is based on the shapes of the fitting plots. The main procedure for parameter estimation of the models is described accordingly. In addition, the range of data plots on WPP is clearly highlighted from the practical point of view. To note this is important as mathematical analysis of a model with neglecting the applicable range of the model plot will incur discrepancy or big errors in model selection and parameter estimates. © 2013 Elsevier Ltd. All rights reserved.

Publication Date


  • 2013

Citation


  • Zhang, T. & Dwight, R. (2013). Choosing an optimal model for failure data analysis by graphical approach. Reliability Engineering and System Safety, 115 111-123.

Scopus Eid


  • 2-s2.0-84875824728

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2308&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1299

Number Of Pages


  • 12

Start Page


  • 111

End Page


  • 123

Volume


  • 115

Abstract


  • Many models involving combination of multiple Weibull distributions, modification of Weibull distribution or extension of its modified ones, etc. have been developed to model a given set of failure data. The application of these models to modeling a given data set can be based on plotting the data on Weibull probability paper (WPP). Of them, two or more models are appropriate to model one typical shape of the fitting plot, whereas a specific model may be fit for analyzing different shapes of the plots. Hence, a problem arises, that is how to choose an optimal model for a given data set and how to model the data. The motivation of this paper is to address this issue. This paper summarizes the characteristics of Weibull-related models with more than three parameters including sectional models involving two or three Weibull distributions, competing risk model and mixed Weibull model. The models as discussed in this present paper are appropriate to model the data of which the shapes of plots on WPP can be concave, convex, S-shaped or inversely S-shaped. Then, the method for model selection is proposed, which is based on the shapes of the fitting plots. The main procedure for parameter estimation of the models is described accordingly. In addition, the range of data plots on WPP is clearly highlighted from the practical point of view. To note this is important as mathematical analysis of a model with neglecting the applicable range of the model plot will incur discrepancy or big errors in model selection and parameter estimates. © 2013 Elsevier Ltd. All rights reserved.

Publication Date


  • 2013

Citation


  • Zhang, T. & Dwight, R. (2013). Choosing an optimal model for failure data analysis by graphical approach. Reliability Engineering and System Safety, 115 111-123.

Scopus Eid


  • 2-s2.0-84875824728

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=2308&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/1299

Number Of Pages


  • 12

Start Page


  • 111

End Page


  • 123

Volume


  • 115