Subspace Approaches to Dynamic
Model Identification and Fault Diagnosis
Prof. Joe Qin, Ph.D.
Zeit: Dienstag, 20. 11. 2001, 16:00
Ort: Hörsaal V 9.31 Pfaffenwaldring 9,
The number of industrial applications of multivariable
model predictive control is reaching 4,600 and is
rapidly growing. A key step in these applications is
to build accurate dynamic models. In this talk we
present some recent development in subspace approaches
for building general dynamic models from process data.
Principal component analysis will be used to achieve
consistent models under input and output errors. The
effectiveness of this approach is demonstrated using
simulation and industrial examples.
The second part of the seminar is concerned with
detecting and identifying sensor faults using subspace
models under dynamic operations. A unique way to
identify faults with maximum sensitivity is presented.
We then show how this method can be used for sensor
validation for an industrial process.
Dr. S. Joe Qin is currently an Associate Professor in
Chemical Engineering at University of Texas at Austin.
He obtained his BS and MS degrees in Automatic Control
from Tsinghua University in Beijing, China, in 1984
and 1987, respectively. His Ph.D. degree is in
Chemical Engineering from University of Maryland. His
current research interests include process monitoring
and fault identification, model predictive control,
run-to-run control, system identification,
microelectronics process monitoring and control,
chemical process monitoring and control, and control
performance monitoring. He is a recipient of an NSF
CAREER Award and is currently an Editor for Control