TITLE: Big Data and Process Quality-relevant Monitoring

SPEAKER: Dr. Joe Qin

ABSTRACT:

Process monitoring provides supervision of process operations so that abnormal operating conditions can be detected, diagnosed, and proper adjustment can be implemented as needed. The ultimate purpose is to reduce process variability under real-world operating conditions with the use of real time data. A focus of this seminar is on the recently rising interest of big data and the use of multivariate statistical methods for efficient data-driven quality-relevant process monitoring. 
 
As an improvement over principal component analysis, a concurrent projection to latent structures is presented for the monitoring of output-relevant faults that affect the quality and input-relevant process faults. The input and output data spaces are concurrently projected to five subspaces, a joint input-output subspace that captures co-variations between input and output, an output-principal subspace, an output-residual subspace, an input principal subspace, and an input-residual subspace. Hotelling’s and residuals-based indices are developed for various fault detection alarms based on these subspaces. The proposed monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output residual subspace, as well as faults that affect the input spaces only.

BIO:  Dr. S. Joe Qin is the Fluor Professor of Process Engineering and Vice Dean at the Viterbi School of Engineering at University of Southern California. He obtained his B.S. and M.S. degrees in Automatic Control from Tsinghua University in Beijing, China, in 1984 and 1987, respectively, and his Ph.D. degree in Chemical Engineering from University of Maryland at College Park in 1992. Dr. Qin’s research interests include statistical process monitoring and fault diagnosis, model predictive control, system identification, run-to-run control, semiconductor process control, and control performance monitoring. He is a Co-Director of the Texas-Wisconsin-California Control Consortium where he has been principal investigator for 17 years. He is a recipient of the National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, a Chang Jiang Professor by the Ministry of Education of China from 2007-2010, and an IFAC Best Paper Prize for the model predictive control survey paper published in Control Engineering Practice. He is currently an Associate Editor for Journal of Process Control, IEEE Control Systems Magazine, and IEEE Transactions on Industrial Informatics, and a Member of the Editorial Board for Journal of Chemometrics. He served as an Editor for Control Engineering Practice and an Associate Editor for IEEE Transactions on Control Systems Technology. He is a Fellow of IEEE. He has published over 100 papers in SCI journals, with over 3600 ISI citations and an ISI h-index of 30.