Conference paper
A multi-category decision support framework for the Tennessee Eastman problem
Institute of Electrical and Electronics Engineers Inc.
2007 European Control Conference (ECC) (KOS, Greece, 02/07/2007–05/07/2007)
2007
Abstract
The paper investigates the feasibility of developing a classification framework, based on support vector machines, with the correct properties to act as a decision support system for an industrial process plant, such as the Tennessee Eastman process. The system would provide support to the technicians who monitor plants by signalling the occurrence of abnormal plant measurements marking the onset of a fault condition. To be practical such a system must meet strict standards, in terms of low detection latency, a very low rate of false positive detection and high classification accuracy. Experiments were conducted on examples generated by a simulation of the Tennessee Eastman process and these were preprocessed and classified using a support vector machine. Experiments also considered the efficacy of preprocessing observations using Fisher Discriminant Analysis and a strategy for combining the decisions from a bank of classifiers to improve accuracy when dealing with multiple fault categories.
Details
- Title
- A multi-category decision support framework for the Tennessee Eastman problem
- Authors/Creators
- G.E. Lee (Author/Creator)P.A. Bahri (Author/Creator)S.S. Shastri (Author/Creator)A. Zaknich (Author/Creator)
- Conference
- 2007 European Control Conference (ECC) (KOS, Greece, 02/07/2007–05/07/2007)
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Identifiers
- 991005540090507891
- Copyright
- © 2007 EUCA
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Conference paper
Metrics
160 File views/ downloads
92 Record Views