Traditional statistical process control approaches are less effective in dealing with multivariate and autocorrelated processes. With the continual increase in process complexity, this inefficiency is becoming more apparent. A special type of multivariate and autocorrelated process is a process occurring within a heterogeneous production environment (a variety of types of machines, pots, etc. used for the same task). This makes the quality control of such processes more difficult. The approach presented in the paper utilizes time series fitting, cluster analysis and association mining in relation to a single data mining model for the analysis of complex multivariate autocorrelated processes. The aim is to divide the production cells (machines, pots, etc.) into groups exhibiting similar behaviors. This can then be used for more effective quality control of the entire process and afterwards to analyze the reasons for this behavior. This paper includes someof the results obtained from applying the model to an actual multivariate high autocorrelated process, the production of primary aluminum using the Hall-Heroult electrolysis process. The Hall-Heroult electrolysis process is a continual process that is ongoing in several pots simultaneously. The average plant operates 300 pots. Therefore, the quality control of such a complex process faces many issues concerning monitoring and problem diagnosis. The paper describes a method for dividing the pots into control groups exhibiting similar behaviors, which can then be used in the planning phase of the quality control analysis and to make improvements within these groups and thereby within the whole process.(original abstract)
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