Abstract：
It is now common to have archival history of thousands of sensors sampled every second over long time periods. Yet we frequently have process engineers complain:
“….We are drowning in data but starving for information…”.
How can these rich data sets be put to use? This seminar will address the issue of information and knowledge extraction from data with emphasis on process and performance monitoring.
Most of the major plant, factory, process, equipment and tool disruptions are avoidable, and yet preventable fault detection and diagnosis strategies are not the norm in most industries. It is not uncommon to see simple and preventable faults disrupt the operation of an entire integrated manufacturing facility. For example, faults such as malfunctioning sensors or actuators, inoperative alarm systems, poor controller tuning or configuration can render the most sophisticated control systems useless. Such disruptions can cost in the excess of $1 million per day and on the average they rob the plant of 7% of its annual capacity.
Over the last decade the fields of multivariate statistics, controller performance monitoring techniques and Bayesian inference methods have merged to develop powerful sensing and condition-based monitoring systems for predictive fault detection and diagnosis. These methods rely on the notion of sensor fusion whereby data from many sensors or units are combined with process information, such as physical connectivity of process units, to give a holistic picture of health of an integrated plant. Such methods are at a stage where these strategies are being implemented for off-line and on-line deployment.
This presentation will outline the field of sensor fusion - the application of signal processing methods, in the temporal as well as spectral domains, on a multitude and NOT singular sensor signals to detect incipient process abnormality before a catastrophic breakdown is likely to occur. This talk will be complemented with industrial case studies to demonstrate the success of these methods. These same techniques can also be applied in other fields. For example, the fusion of pixels of information from digital images will be illustrated via application of automated detection and diagnosis of Malaria parasites from microscopic images.
Speaker Details：
Sirish L. Shah has been with the University of Alberta since 1978, where he currently holds the NSERC-Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control. He was the recipient of the Albright & Wilson Americas Award of the Canadian Society for Chemical Engineering (CSChE) in recognition of distinguished contributions to chemical engineering in 1989, the Killam Professor in 2003 and the D.G. Fisher Award of the CSChE for significant contributions in the field of systems and control. He has held visiting appointments at Oxford University and Balliol College as a SERC fellow , Kumamoto University (Japan) as a senior research fellow of the Japan Society for the Promotion of Science (JSPS) , the University of Newcastle, Australia, IIT-Madras India and the National University of Singapore. The main area of his current research is process and performance monitoring, system identification and design and implementation of softsensors. He has co-authored two books, the first titled, Performance Assessment of Control Loops: Theory and Applications, and a more recent book titled ‘Diagnosis of Process Nonlinearities and Valve Stiction: Data Driven Approaches”. |
