This paper describes a methodology for the simulation of multivariate out of control situations using in-control data. The method is based on finding the independent factors of the variability of the process, and shifting these factors one by one. These shifts are then translated in terms of the observed variables. The shifts provoked by the most important factors are called principal alarms. The principal alarms are plotted, visualizing the main deviations of the process. Also, a resampling procedure for ARL estimation using principal alarms is proposed. An application using a real industrial process, illustrates the usefulness of the methodology.
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