Generation of Signed Directed Graphs Using Functional Models

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

Documents

View graph of relations

Intelligent fault diagnosis systems can be a major aid to human operators charged with the high-level control of industrial plants. Such systems aim for high diagnostic accuracy while retaining the ability to produce results that can be interpreted by human experts on site. Signed directed graphs have been shown to be a viable method for plant-wide diagnosis that can incorporate both quantitative information about the process condition as well as qualitative information about the system topology and the functions of its components. Their range of application in industrial settings has been limited due to difficulties regarding the interpretation of results and consistent graph generation. This contribution addresses these issues by proposing an automated generation of signed directed graphs of industrial processes in the chemical, petroleum and nuclear industries using Multilevel Flow Modeling; a functional modeling method designed for operator support. The approach is demonstrated through a case study conducted on the Tennessee Eastman Process, showing that Multilevel Flow Modeling can facilitate a consistent modeling process for signed directed graphs. Finally, the resulting benefits regarding qualitative reasoning for plant-wide diagnosis are discussed.
Original languageEnglish
Book seriesIFAC-PapersOnLine
Number of pages8
ISSN2405-8963
Publication statusAccepted/In press - 2019
Event5th IFAC Conference on Intelligent Control and Automation Sciences - Riddel Hall, Belfast, United Kingdom
Duration: 21 Aug 201923 Aug 2019
http://www.qub.ac.uk/sites/icons2019/

Conference

Conference5th IFAC Conference on Intelligent Control and Automation Sciences
LocationRiddel Hall
CountryUnited Kingdom
CityBelfast
Period21/08/201923/08/2019
Internet address

    Research areas

  • Human supervisory control, Fault diagnosis, Directed graphs, Intelligent knowledge-based systems, Decision support systems, Human-centered design

Download statistics

No data available

ID: 191601027