ICONS 2019 Paper Abstract


Paper WeA2FD1.3

Reinartz, Christopher (Technical University of Denmark), Kirchhübel, Denis (Technical University of Denmark), Ravn, Ole (Technical University of Denmark), Lind, Morten (Technical University of Denmark)

Generation of Signed Directed Graphs Using Functional Models

Scheduled for presentation during the Regular Session "Fault Detection, Diagnosis and Fault-tolerant Control I" (WeA2FD1), Wednesday, August 21, 2019, 11:30−11:50,

5th IFAC International Conference on Intelligent Control and Automation Sciences, August 21-23, 2019, Queen’s University Belfast, Northern Ireland

This information is tentative and subject to change. Compiled on October 16, 2021

Keywords Diagnosis, fault detection and fault tolerant control, Modeling and identification, Process control


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.


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