| Distinguished Lecture 3 | Some Remarks on Basic Multivariate Statistical Analysis Approaches for Fault Detection |
| Date/Time | Tuesday, May 31, 2016 14:50-15:30 |
| Venue | International Leture Hall of the 2nd floor |
| Presenter | Prof. Shen Yin, Harbin Institute of Technology, China |
Abstract
Due to the increasing demands on system performance, production quality as well as economic operation, modern technical systems become more complicated and the automation degrees are significantly growing. To ensure the safety and reliability of such complicated processes, an effective fault detection system is of prime importance in process industry nowadays. Although the model-based fault detection theory has been well established, it is still difficult to establish mathematical model by means of the first principles for large-scale process. On the other hand, a large amount of historical data from regular sensor measurements, event-logs and records are often available in such industrial processes. Motivated by this observation, it is of great interest to design fault detection schemes only based on the available process data. This talk is dedicated to the modifications on the standard multivariate statistical process monitoring approaches. The modified approaches are considerably simple, and most importantly, avoid the drawbacks of the standard techniques. As a result, the proposed approaches are able to provide enhanced fault detection performance on the applications under stationary operating conditions.
Biography
Hong Chenreceived the B.E. degree in automation from Harbin Institute of ,Technology, Harbin, China, in 2004, the M.Sc. degree in control and information system and the Ph.D. degree in electrical engineering and information technology from University of Duisburg-Essen, Duisburg, Germany. His research interests are model based and data driven fault diagnosis, fault tolerant control and big data focused on industrial electronics. He is currently a Professor at School of Astronautics, Harbin Institute of Technology and co-director of Institute of Intelligent Control and Systems. He is a Senior Member of IEEE and the Chair of Technical Committee on "Data-driven Control and Monitoring" of IEEE Industrial Electronics Society. He served as Guest Editors to organize several special sections on Proceedings of the IEEE, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Industrial Electronics etc. He is currently Associate Editor for IEEE Transactions on Industrial Electronics, Journal of the Franklin Institute, IE Technology News, Neurocomputing, Journal of Intelligent and Fuzzy Systems, International Journal of Applied Mathematics and Computer Science etc.