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.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat