报告人：Dr. Yi Cao (CranfieldUniversity,UK)

报告地点：控制学院工程中心211室

报告时间：10月21日下午13:00 -15:00

Abstract:

The detection and diagnosis of faults in industrial processes can help to reduce maintenance costs and improve the safety of plant operations. In order to avoid inefficient operation of faulty equipment and minimize the number of unnecessary shutdowns it is important for the operators to have reliable information about the current impact of the fault in the process performance and the future evolution of the fault. This can be a complex topic when dealing with systems working under varying operating conditions. Canonical Variate Analysis (CVA) is a multivariate algorithm for process monitoring which has the ability to capture process dynamics more efficiently than other similar methods. The aim of this work is to demonstrate the capability of CVA to extract reliable information about the process condition and the effects of the faults in the system performance using experimental data. The data sets were acquired from a large experimental multiphase flow facility representing a real small-scale multiphase flow separation process. Different faults were introduced in the system to assess the performance of CVA in terms of fault detection and diagnosis, and also to model the system behaviour under normal and faulty conditions. The results suggest that CVA is a very reliable tool for fault detection and diagnosis, as well as for the identification of the system behaviour.

报告人简介：

Yi Cao is a Reader in Control Systems Engineering, Cranfield University. He Obtained PhD in Control Engineering from the University of Exeter in 1996, MSc in Industrial Automation from Zhejiang University, China in 1985. His main research interest is in developing systematic approaches to solve various operational problems involved in industrial processes using both models and data. Dr. Cao is the main inventor of the Inferential Slug Control technology to mitigate slugging of multiphase flow in offshore oil and gas production systems. A successful field trial has showed that the technology was able to increase oil production by 10%. This achievement received the Innovation Award from the East England Energy Group (EEEgr) in 2010. His recent research is focusing on data driven self-optimizing control methodology. By applying it to water flooding process for oil enhanced recovery, it can achieve near optimal operation in spite of the uncertainties of oil reservoirs. His research also covers data driven condition monitoring approaches for fault diagnosis an prognosis.