liu.seSearch for publications in DiVA
Change search
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
A Combined Data-Driven and Model-Based Residual Selection Algorithm for Fault Detection and Isolation
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0808-052X
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 27, no 2, p. 616-630Article in journal (Refereed) Published
Abstract [en]

Selecting residual generators for detecting and isolating faults in a system is an important step when designing model-based diagnosis systems. However, finding a suitable set of residual generators to fulfill performance requirements is complicated by model uncertainties and measurement noise that have negative impact on fault detection performance. The main contribution is an algorithm for residual selection that combines model-based and data-driven methods to find a set of residual generators that maximizes fault detection and isolation performance. Based on the solution from the residual selection algorithm, a generalized diagnosis system design is proposed where test quantities are designed using multivariate residual information to improve detection performance. To illustrate the usefulness of the proposed residual selection algorithm, it is applied to find a set of residual generators to monitor the air path through an internal combustion engine.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 27, no 2, p. 616-630
Keywords [en]
Automotive applications, change detection algorithms, fault detection, fault diagnosis, machine learning.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-149582DOI: 10.1109/TCST.2017.2773514ISI: 000457619300014OAI: oai:DiVA.org:liu-149582DiVA, id: diva2:1231587
Note

Funding agencies: Volvo Car Corporation Gothenburg Sweden

Available from: 2018-07-08 Created: 2018-07-08 Last updated: 2019-02-20Bibliographically approved

Open Access in DiVA

fulltext(9821 kB)292 downloads
File information
File name FULLTEXT01.pdfFile size 9821 kBChecksum SHA-512
6165414ab5aec91de0548771cc74a1f5d62c5d304eb2f1bf05d6112455049b14eabd83a71107cbfc7d08c03916ecfccc1c58ded4893ee3cd643c6ff37b233be4
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Jung, DanielSundström, Christofer

Search in DiVA

By author/editor
Jung, DanielSundström, Christofer
By organisation
Vehicular SystemsFaculty of Science & Engineering
In the same journal
IEEE Transactions on Control Systems Technology
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 292 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 383 hits
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