Predicting paper making defects on-line using data mining

doi:10.1016/S0950-7051(98)00061-6

Predicting paper making defects on-line using data mining

1998-11-01

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DOI：10.1016/S0950-7051(98)00061-6

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This paper describes an application that was jointly developed by Caledonian Paper and Intelligent Applications for the early prediction of paper defects from process data, so that corrective action 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 in 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. (C) 1998 Elsevier Science B.V. All rights reserved.

Keywords:

predicting on-line

paper making defects

data mining

Predicting paper making defects on-line using data mining

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