ENBIS: European Network for Business and Industrial Statistics

On-line Monitoring and Classification of Paper Formation using Image Analysis

Abstract

Submitted by

Marco P. Seabra dos Reis

Authors

Marco S. Reis, Armin Bauer

Affiliation

University of Coimbra, Voith Paper Automation

Abstract

Paper formation (the distribution and intermixing of fibres in a paper sheet), plays a central role in paper products, and is usually evaluated off-line, with a significant delay relative to the high production rates achieved in modern paper machines.

In this work, we address an approach for evaluating and monitor paper formation using images acquired with an especially designed sensor, in-line, in-situ and in real time. The methodology essentially consists of applying wavelet texture analysis (WTA) to raw images (Bharati et al., 2004), in order to compute a wavelet signature vector for each image, based on which the discrimination of images regarding different formation quality levels can be performed. A principal component analysis (PCA; Jackson, 1991) of such features confirms the differences in formation quality levels defined a priori, from visual inspection, and, furthermore, suggests a new subclass for abnormal samples, related to the bulkiness of fibre flocks.

A PCA-MSPC monitoring approach is also proposed, providing good preliminary results when applied to the available images, as analyzed with the ROC curve for the method and confirmed with a Monte Carlo study using subimages with 1/4 of the size of the original ones.

References
Bharati, M. H., Liu, J. J. & MacGregor, J. F. (2004). Image Texture Analysis: Methods and Comparisons. Chemometrics and Intelligent Laboratory Systems, 72, 57-71.

Jackson, J. E. (1991). A User's Guide to Principal Components. New York: Wiley.

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