A Kernel Based Multi-resolution Time Series Analysis for Screening Deficiencies in Paper Production
This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions.
KeywordsDiscrete Wavelet Transform Detection Accuracy Paper Mill Paper Structure Novelty Detector
Unable to display preview. Download preview PDF.
- 1.Keller, D.S., Lewalle, J., Luner, P.: Wavelet Analysis of Simulated Paper Formation. Paperi ja Puu 81(7), 499–505 (1999)Google Scholar
- 3.Nesic, Z., Davies, M., Dumont, G.: Paper Machine Data Analysis and Compression using Wavelets. Tappi Journal 80(10), 191–204 (1997)Google Scholar
- 4.Timberlake, A., Strom, E.: Do You Know What Causes the Variability in the Paper You Produce? In: TAPPI Proceedings of 2004 Paper Summit, Spring Technical & International Environmental Conference (2004)Google Scholar
- 6.Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar