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In most real-world manufacturing systems, the production of goods comprises several autocorrelated stages and the quality characteristics of the goods at each stage are correlated random variables. This paper addresses the problem of monitoring a multivariate-multistage manufacturing process and diagnoses the possible causes of out-of-control signals. To achieve this purpose using multivariate time series models, first a model for the autocorrelated data coming from multivariate-multistage processes is developed. Then, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages. In-control and out-of-control average... 

knowing the time of a process change would simplify the search, identification, and removal of the special causes that disturbed the process. Since, in many real world manufacturing systems, the production of goods comprises several autocorrelated stages; in this paper, the problem of the change point estimation for such processes is addressed. A first order autoregressive model (AR(1)) is used to model a multistage process observations, where a X -chart is established for monitoring its mean. A step change is assumed for the location parameter of the model. After receiving an out-of-control signal, in order to determine the stage and the sample that caused the change (hence finding the time... 

Knowing the time of change would narrow the search to find and identify the variables disturbing a process. The knowledge of the change point can greatly aid practitioners in detecting and removing the special cause(s). Count processes with an autocorrelation structure are commonly observed in real-world applications and can often be modeled by the first-order integer-valued autoregressive (INAR) model. The most widely used marginal distribution for count processes is Poisson. In this study, change-point estimators are proposed for the parameters of correlated Poisson count processes. To do this, Newton's method is first used to approximate the parameters of the process. Then, maximum...