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Engine Fault Diagnosis Combining Model-based Residuals and Data-Driven Classifiers
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0808-052X
2019 (English)In: IFAC PAPERSONLINE, ELSEVIER , 2019, Vol. 52, no 5, p. 285-290Conference paper, Published paper (Refereed)
Abstract [en]

Design of fault diagnosis systems is complicated by limited training data and inaccuracies in physical-based models when designing fault classifiers. A hybrid fault diagnosis approach is proposed using model-based residuals as input to a set of data-driven fault classifiers. As a case study, sensor data from an internal combustion engine test bed is used where faults have been injected into the system and a physical-based mathematical model of the air flow through the engine is available. First, a feature selection algorithm is applied to find a minimal set of residuals that is able to separate the different fault modes. Then, two different fault classification approaches are discussed, Random Forests and one-class Support Vector Machines. A set of one-class Support Vector Machines is used to model data from each fault mode separately. The case study illustrates an advantage of using one-class classifiers, which makes it possible to detect unknown faults by identifying samples not belonging to any known fault mode. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER , 2019. Vol. 52, no 5, p. 285-290
Keywords [en]
Fault diagnosis; Model-based diagnosis; Machine learning; Random Forests; Support Vector Machines
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-161218DOI: 10.1016/j.ifacol.2019.09.046ISI: 000486629500047OAI: oai:DiVA.org:liu-161218DiVA, id: diva2:1365647
Conference
9th IFAC International Symposium on Advances in Automotive Control (AAC)
Available from: 2019-10-25 Created: 2019-10-25 Last updated: 2019-10-25

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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