This paper describes an application that was jointly developed by Caledonian Paper and Inrellgent Apphcations Ltd for the early prediction of paper defects from process data, so that corrective acution can be applied before the defect becomes too significant. Correlations between process data and past faults were extracted and then programmed into an on-line predictive software model which is able to analyse current process data m real time, looking for bad patterns which may lead to defects in the paper. Depending on the degree of severity of defect that the model predicts, and the nature of the developing problem, the machine operators can take steps to prevent the defect from becoming so significant as to result in salvage. This article describes the way the application was developed and shows how data mining can be successfully applied to the paper industry.
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