Exceedance probability estimation for a quality test consisting of multiple measurements
S Tamminen, I Juutilainen, J Röning - Expert systems with applications, 2013 - Elsevier
S Tamminen, I Juutilainen, J Röning
Expert systems with applications, 2013•ElsevierThe purpose of this study was to develop methods for exceedance probability estimation in
the case of highly scattered measurement sets. The situation may occur when product
quality is verified with several test samples, and thus, traditional point prediction based
modelling methods are not sufficient. Density forecasting methods are needed when not
only the mean but also the deviance and the distribution shape of the response depend on
the explanatory variables. Furthermore, with probability predictors, the ranking methods for …
the case of highly scattered measurement sets. The situation may occur when product
quality is verified with several test samples, and thus, traditional point prediction based
modelling methods are not sufficient. Density forecasting methods are needed when not
only the mean but also the deviance and the distribution shape of the response depend on
the explanatory variables. Furthermore, with probability predictors, the ranking methods for …
Abstract
The purpose of this study was to develop methods for exceedance probability estimation in the case of highly scattered measurement sets. The situation may occur when product quality is verified with several test samples, and thus, traditional point prediction based modelling methods are not sufficient.
Density forecasting methods are needed when not only the mean but also the deviance and the distribution shape of the response depend on the explanatory variables. Furthermore, with probability predictors, the ranking methods for the model selection should be chosen carefully, when models trained with different methods are compared.
In this article, the impact toughness of the steel products was modelled. The rejection probability in Charpy-V quality test was predicted with mean and deviation models, distribution shape model and quantile regression model. The proposed methods were employed in two steel manufacturing applications with different distributional properties.
Elsevier
以上显示的是最相近的搜索结果。 查看全部搜索结果