Estimating production test properties from test measurement data

Abstract

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.