integration of machine learning techniques and control charts in multivariate processes

Using multivariate control chart instead of univariate control chart for all variables in processes provides more time and labor advantages that are of significance in the relations among variables. however, the statistical calculation of the measured values for all variables is regarded as a single value in the control chart. therefore, it is necessary to determine which variable(s) are the cause of the out-of-control signal. effective corrective measures can only be developed when the causes of the fault(s) are correctly determined. the present study was aimed at determining the machine learning techniques that could accurately estimate the fault types. through the hotelling t ² chart, out-of- control signals were identified and the types of faults affected by the variables were specified. various machine learning techniques were used to compare classification performances. the developed model was employed in the evaluation of paint quality in a painting process. artificial neural networks (anns) was determined as the most successful technique in terms of the performance criteria. the novelty of this study lies in its classification of the faults according to their types instead of those of the variables. defining the faults based on their types facilitates taking effective and corrective measures when needed.