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                                                                                  控制系學術講座: Process Quality-relevant Monitoring
                                                                                  時間:2013-01-09 來源:綜合辦 編輯:zhbgs 訪問次數:1120

                                                                                  報告題目: Process Quality-relevant Monitoring

                                                                                  報告人:  Prof. S. Joe Qin (USA)


                                                                                  報告時間:1月11日下午3:00 – 4:00

                                                                                  報告地點:控制工程大樓207室(原教九)

                                                                                   

                                                                                  Abstract:
                                                                                  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. In this talk, 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 a latent structure of 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. Appropriate monitoring 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 space only.

                                                                                   

                                                                                  報告人簡介:
                                                                                  Dr. S. Joe Qin is the Fluor Professor at the Viterbi School of Engineering at the University of Southern California and Chang Jiang Professor affiliated with Tsinghua University by the Ministry of Education of China. He obtained his BS and MS 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 in 1992. He worked as a Principal Engineer at Fisher-Rosemount from 1992 to 1995.
                                                                                  His research interests include system identification, process monitoring and fault diagnosis, model predictive control, run-to-run control, semiconductor process control, and control performance monitoring. He is a recipient of the National Science Foundation CAREER Award, DuPont Young Professor Award, Halliburton/Brown & Root Young Faculty Excellence Award, NSF-China Outstanding Young Investigator Award, 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 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.