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Combining AI, FDI, and Statistical Hypothesis-Testing in a Framework for Diagnosis
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.
2003 (English)In: Proceedings of IFAC Safeprocess’03, Elsevier, 2003, Vol. 36, 813-818 p.Conference paper, Published paper (Refereed)
Abstract [en]

A new framework for model based diagnosis is presented using ideas from AI, FDI, and statistical hypothesis testing. The isolation mechanism is based on AI methods, and the main advantage is that multiple faults are handled implicitly. Thus, no special care for isolation of multiple faults is needed. The methods for residual generation, developed in the field of control theory (FDI), can within the framework be fully utilized. Since the framework is also based upon statistical hypothesis testing, it is suitable for problems including noise.

Place, publisher, year, edition, pages
Elsevier, 2003. Vol. 36, 813-818 p.
Series
IFAC Proceedings Volumes, ISSN 1474-6670
Keyword [en]
Structural Methods, Diagnosis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-138078DOI: 10.1016/S1474-6670(17)36593-XOAI: oai:DiVA.org:liu-138078DiVA: diva2:1106710
Conference
IFAC Fault Detection, Supervision and Safety of Technical Processes, Washington, D.e., USA, 2003
Available from: 2017-06-08 Created: 2017-06-08 Last updated: 2017-06-20

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Nyberg, MattiasKrysander, Mattias
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf