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Choosing an optimal model for failure data analysis by graphical approach

机译:通过图形方法选择故障数据分析的最佳模型

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摘要

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.
机译:已开发出许多模型,这些模型涉及多个Weibull分布的组合,Weibull分布的修改或其扩展的扩展等,以对给定的一组失效数据进行建模。这些模型在给定数据集建模中的应用可以基于在威布尔概率纸(WPP)上绘制数据。其中,两个或多个模型适合于模拟拟合图的一种典型形状,而特定模型可能适合于分析图的不同形状。因此,出现了一个问题,即如何为给定的数据集选择最佳模型以及如何对数据建模。本文的动机是解决这个问题。本文总结了具有三个以上参数的威布尔相关模型的特征,包括涉及两个或三个威布尔分布的截面模型,竞争风险模型和混合威布尔模型。本文讨论的模型适合于对WPP上的图形形状可以是凹形,凸形,S形或反S形的数据进行建模。然后,提出了一种基于拟合图形状的模型选择方法。相应地描述了模型参数估计的主要过程。另外,从实际的角度来看,WPP上的数据图范围很明显。要注意这一点很重要,因为忽略模型图的适用范围对模型进行数学分析会在模型选择和参数估计中产生差异或较大的误差。

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