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Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0808-052X
Inst. of Software-integrated Systems, Vanderbilt Univ., USA.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
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2015 (English)In: Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes Safeprocess'15, Elsevier, 2015, Vol. 48, no 21, 1289-1296 p.Conference paper, Published paper (Refereed)
Abstract [en]

Most model-based diagnosis approaches reported in the literature adopt a generic architecture and approach. However, the fault hypotheses generated by these methods may differ. This is not only due to the methods, but also on the basic assumptions made by different diagnostic algorithms on fault manifestation and evolution. While comparing different diagnosis approaches, the assumptions made in each case will have a significant effect on fault diagnosability performance and must therefore also be taken into consideration. Thus, to make a fair comparison, the different approaches should be designed based on the same assumptions. This paper studies the relation between a set of commonly made assumptions and fault isolability performance in order to compare different diagnosis approaches. As a case study, five developed diagnosis systems for a wind turbine benchmark problem are evaluated to analyze the type of assumptions that are applied in the different designs.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 48, no 21, 1289-1296 p.
Series
IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X ; Vol. 48, Issue 21
Keyword [en]
Model-based diagnosis, fault detection and isolation, fault diagnosability analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-137772DOI: 10.1016/j.ifacol.2015.09.703Scopus ID: 2-s2.0-84992486744OAI: oai:DiVA.org:liu-137772DiVA: diva2:1101705
Conference
9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes Safeprocess'15, 2-4 September, Paris, FRANCE
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2017-06-08Bibliographically approved

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Jung, DanielFrisk, ErikKrysander, Mattias

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  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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  • de-DE
  • en-GB
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Output format
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