This preliminary program is subject to change.

Process Monitoring, Fault Detection, and Diagnosis I


Description:
The emphasis of this session is on theoretical and application studies related to fault detection and diagnosis for batch and continuous processes. Higher priority will be given to industrial contributions and real-time studies. Contributions are sought in, but not limited to, the following areas: Novel techniques or advances in existing techniques in fault detection, identification, and diagnosis. Process monitoring system for flexible manufacturing plants with multiple product recipes and various operating conditions. Incorporation of process knowledge into fault detection, identification, and diagnosis. Model maintenance issues (such as adaptive model, model update, etc.) for on-line applications, data pre-processing issues (such as outlier detection, sensor validation, filtering, missing value estimation, etc.) for on-line applications.

Sponsor:
Computers in Operations and Information Processing
Co-Sponsor(s):
Systems and Process Control (10B)


Chair:
Jie Yu
Assistant Professor
McMaster University
Chemical Engineering
1280 Main Street West
Hamilton, ON L8S 4L7
Phone Number: 512-206-6476
Email: jieyu@mcmaster.ca

Co-Chair:
Prashant Mhaskar
Assistant Professor
McMaster University
Department of Chemical Engineering
Hamilton, ON L8S 4L7
Email: mhaskar@mcmaster.ca


Papers:
Fault Detection and Isolation and Fault Tolerant Control of a Catalytic Alkylation of Benzene Process
David Chilin, Univ. of California, Los Angeles; Jinfeng Liu, University of California, Los Angeles; Xianzhong Chen, Univ. of California, Los Angeles; Panagiotis D. Christofides, University of California, Los Angeles
Industrial Experiences of On-Line Fault Detection and Process Monitoring
Zdravko Stefanov, The Dow Chemical Company; Leo H. Chiang, The Dow Chemical Company
Fault Detection and Accommodation in Particulate Processes Using Multi-Rate Sampled and Delayed Measurements
Trina Napasindayao, University of California, Davis; Nael H. El-Farra, University of California, Davis
Optimal Sensor Placement for Fault Diagnosis in an Acid Gas Removal (AGR) Unit for an Integrated Gasification Combined Cycle (IGCC) Plant with CO2 Capture
M. R. Islam, Texas Tech University; Jeevan Maddala, Texas Tech University; Richard Turton, West Virginia University; Debangsu Bhattacharyya, West Virginia University; Raghunathan Rengaswamy, Texas Tech University
Fault Detection Based On Kernel Mean Discrepancy Test
Jiusun Zeng, Institute of cyber systems and control, zhejiang University; Lei Xie, Zhejiang university
See more of this Group/Topical: Computing and Systems Technology Division