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Residual Selection for Consistency Based Diagnosis Using Machine Learning Models
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4965-1077
2018 (English)In: IFAC PAPERSONLINE, ELSEVIER SCIENCE BV , 2018, Vol. 51, no 24, p. 139-146Conference paper, Published paper (Refereed)
Abstract [en]

A common architecture of model-based diagnosis systems is to use a set of residuals to detect and isolate faults. In the paper it is motivated that in many cases there are more possible candidate residuals than needed for detection and single fault isolation and key sources of varying performance in the candidate residuals are model errors and noise. This paper formulates a systematic method of how to select, from a set of candidate residuals, a subset with good diagnosis performance. A key contribution is the combination of a machine learning model, here a random forest model, with diagnosis specific performance specifications to select a high performing subset of residuals. The approach is applied to an industrial use case, an automotive engine, and it is shown how the trade-off between diagnosis performance and the number of residuals easily can be controlled. The number of residuals used are reduced from original 42 to only 12 without losing significant diagnosis performance. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2018. Vol. 51, no 24, p. 139-146
Keywords [en]
Consistency-based diagnosis; residuals; machine learning; random forests; variable importance; diagnosis performance
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-152642DOI: 10.1016/j.ifacol.2018.09.547ISI: 000447016900021OAI: oai:DiVA.org:liu-152642DiVA, id: diva2:1262059
Conference
10th International-Federation-of-Automatic-Control (IFAC) Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
Available from: 2018-11-09 Created: 2018-11-09 Last updated: 2019-09-23

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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