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Residual selection for fault detection and isolation using convex optimization
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering. The Ohio State University, Columbus, OH, USA.ORCID iD: 0000-0003-0808-052X
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 97, p. 143-149Article in journal (Refereed) Published
Abstract [en]

In model-based diagnosis there are often more candidate residual generators than what is needed and residual selection is therefore an important step in the design of model-based diagnosis systems. The availability of computer-aided tools for automatic generation of residual generators have made it easier to generate a large set of candidate residual generators for fault detection and isolation. Fault detection performance varies significantly between different candidates due to the impact of model uncertainties and measurement noise. Thus, to achieve satisfactory fault detection and isolation performance, these factors must be taken into consideration when formulating the residual selection problem. Here, a convex optimization problem is formulated as a residual selection approach, utilizing both structural information about the different residuals and training data from different fault scenarios. The optimal solution corresponds to a minimal set of residual generators with guaranteed performance. Measurement data and residual generators from an internal combustion engine test-bed is used as a case study to illustrate the usefulness of the proposed method.

Place, publisher, year, edition, pages
Pergamon Press, 2018. Vol. 97, p. 143-149
Keywords [en]
Fault detection and isolation, Feature selection, Model-based diagnosis, Convex optimization, Computer-aided design tools
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-151295DOI: 10.1016/j.automatica.2018.08.006ISI: 000447568400016Scopus ID: 2-s2.0-85051683130OAI: oai:DiVA.org:liu-151295DiVA, id: diva2:1248548
Available from: 2018-09-17 Created: 2018-09-17 Last updated: 2018-11-09Bibliographically approved

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The full text will be freely available from 2020-08-18 11:24
Available from 2020-08-18 11:24

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Jung, DanielFrisk, Erik

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf