|dc.contributor.advisor||Qin, S. Joe||en
|dc.creator||Yu, Jie, 1977-||en
|dc.description.abstract||Due to the industrial value, control performance and process monitoring
have attracted increasing attention in recent years. However, there still exist
challenges that restrict the industrial applications of monitoring technology.
This dissertation presents some innovative solutions to the monitoring issues.
To avoid the interactor requirement of minimum variance control (MVC)
benchmark, a data-driven covariance monitoring framework is established.
Relative to a user-defined benchmark, generalized eigenvalue analysis is employed
to extract the directions with the worst performance. A statistical inference
strategy is then developed to identify the worse or better performance
directions and subspace. The covariance based indices are further derived to
assess the performance degradation or improvement. To diagnose the controlled
variables causing the performance change, two types of multivariate
contribution methods are proposed. One is to evaluate the significance of
the eigenvector loadings while the other to examine the angle between each
variable and the worse/better subspace.
Complementary to the data-driven performance monitoring scheme, a
simplified solution to MVC benchmark is also developed. A right diagonal
interactor is first factorized from process time delays and the corresponding
MVC benchmark is derived with numerical simplicity. For more general MIMO
processes, left and right diagonal interactors are integrated to characterize the
more complex delay structure. The MVC estimation using the left/right diagonal
interactors are presented. To further improve multivariable control
performance, an iterative strategy of output weighting selection is proposed.
Eigenvalue decomposition is implemented on the output covariance to find the
directions with the largest variance inflation. A nondiagonal weighting matrix
is then designed with respect to the eigendirections and more importance
proportional to the corresponding eigenvalues is assigned.
In addition to control performance monitoring, process monitoring is
also investigated with focus on fault detection and diagnosis of multistage
overlay lithography processes. In our work, a multistage state-space model
for the misalignment errors is developed from the physical principles and then
formulated into the general mixed linear model. Subsequently, variance component
analysis is employed to estimate the mean and variance components
of the potential fault sources. A hypothesis testing procedure is adopted to
detect the active faults in different layers while the mean/variance estimates
are used to diagnose their magnitude and orientation.||
|dc.rights||Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.||en
|dc.subject||Quality control--Statistical methods||en
|dc.subject||Fault location (Engineering)--Statistical methods||en
|dc.subject||Analysis of covariance||en
|dc.subject||Analysis of variance||en
|dc.title||Data-driven approach for control performance monitoring and fault diagnosis||en
|thesis.degree.grantor||The University of Texas at Austin||en
|thesis.degree.name||Doctor of Philosophy||en