Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation

In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. in such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. in this paper, first we use an artificial neural network (ann)-based method in the literature for detecting the variance shifts as well as diagnosing the sources of variation in the multivariate-attribute processes. then, based on the quality characteristics responsible for the out-of-control state, we propose a modular model based on the ann for estimating the time of step change in the multivariate-attribute process variability. we also compare the performance of the annbased estimator with the estimator based on maximum likelihood method (mle). a numerical example based on simulation study is used to evaluate the performance of the estimators in terms of the accuracy and precision criteria. the results of the simulation study show that the proposed ann-based estimator outperforms the mle estimator under different out-of-control scenarios where different shift magnitudes in the covariance matrix of multivariateattribute quality characteristics are manifested.