Statistics Imperfect repair Imperfect test Gaussian mixture model parameter estimation
Simon P. Wilson, Suresh Goyal, Estimating production test properties from test measurement data, Applied Stochastic Models in Business and Industry, 2011
Applied Stochastic Models in Business and Industry;
A complex sequence of tests on components and the system is a part of many manufacturing processes. Statistical imperfect test and repair models can be used to derive the properties of such test sequences but require model parameters to be specified. We describe a technique for estimating such parameters from typical data that are available from past testing. A Gaussian mixture model is used to model the wide variety of statistical properties of test data, including outliers, multi-modality and skewness, from which test properties are derived. Model fitting
is through a Bayesian approach, implented by Markov chain Monte Carlo.
Please note: There is a known bug in some browsers that causes an
error when a user tries to view large pdf file within the browser window.
If you receive the message "The file is damaged and could not be
repaired", please try one of the solutions linked below based on the
browser you are using.
Items in TARA are protected by copyright, with all rights reserved, unless otherwise indicated.