Premium
Estimating production test properties from test measurement data
Author(s) -
Wilson Simon P.,
Goyal Suresh
Publication year - 2011
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.930
Subject(s) - outlier , computer science , markov chain monte carlo , bayesian probability , statistical model , statistical hypothesis testing , test data , test (biology) , statistics , data mining , econometrics , mathematics , artificial intelligence , paleontology , biology , programming language
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 illustrate the approach and as a model that can represent the wide variety of statistical properties of test data, including outliers, multimodality and skewness. Model fitting was carried out using a Bayesian approach, implemented by MCMC. Copyright © 2011 John Wiley & Sons, Ltd.