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Paper machine data analysis using wavelets

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Title: Paper machine data analysis using wavelets
Author: Nesic, Zoran
Degree: Master of Applied Science - MASc
Program: Electrical and Computer Engineering
Copyright Date: 1996
Issue Date: 2009-02-06
Series/Report no. UBC Retrospective Theses Digitization Project [http://www.library.ubc.ca/archives/retro_theses/]
Abstract: The thesis describes a new approach to paper machine process data analysis using one-dimensional and two-dimensional discrete wavelet transforms. These techniques have been adapted from a general theory that has been developed in recent years on the application of wavelets to signal analysis. Application areas in which the theory was first applied have included image processing and bandwidth compression for communications. Two main applications of the discrete wavelet transform have been analyzed in this thesis. First, an analysis of the use of wavelets for processing scanned data representing basis weight and moisture variations on a paper machine has been carried out. It has been shown that wavelets are effective for the detection of process signals in noisy data, so leading to better estimation and visualization of the machine direction and cross machine variations in process data. The second main application of the method has been to allow significant compression of the process data without diminishing the ability to reconstruct accurate profiles. It has been shown that the compression method can be embedded into the estimation algorithm, producing excellent results without a major expense in computation time. It has been shown that, in both applications, the new methods produce results superior to the industrially accepted procedures. For appropriate choice of wavelets, profile estimates are improved over those obtained using exponential filtering or other standard analysis methods. The data compression technique presents a new concept in paper machine data analysis and the author is not aware of any previous references to this subject. The ability to reduce data storage requirements is of importance in mill-wide process monitoring systems. A comprehensive analysis of the proposed algorithms has been carried out on a variety of simulated data sets for which the true process variations are known. Industrial data has also been analyzed and it is apparent that the method had many desirable characteristics.
Affiliation: Applied Science, Faculty of
URI: http://hdl.handle.net/2429/4243
Scholarly Level: Graduate

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