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 Comparison of Two Methods for Stochastic Fault Detection: the Parity Space Approach and Principal Component Analysis
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2004 (English)Report (Other academic)
Abstract [en]

This paper compares two methods for fault detection and isolation in a stochastic setting. We assume additive faults on input and output signals, and stochastic unmeasurable disturbances. The first method is the parity space approach, analyzed in a stochastic setting. The stochastic parity space approach is similar to a Kalman filter, but uses an FIR fiter, while the Kalman filter is IIR. This enables faster response to changes. The second method is to use PCA, principal component analysis. In this case no model is needed, but fault isolation will be more difficult. The methods are illustrated on a simulation model of an F-16 aircraft. The fault detection probabilities can be calculated explicitly for the parity space approach, and are verified by simulations. The simulations of the PCA method suggest that the residuals have similar fault detection and isolation capabilities as for the stochastic parity space approach.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2004. , 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2636
Keyword [en]
Fault detection, Fault isolation, Diagnosis, Kalman filtering, Adaptive filters, Linear systems, Parity space, Principal components analysis, PCA
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55812ISRN: LITH-ISY-R-2636OAI: oai:DiVA.org:liu-55812DiVA: diva2:316518
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-08-19Bibliographically approved

Open Access in DiVA

fulltext(268 kB)179 downloads
File information
File name FULLTEXT01.pdfFile size 268 kBChecksum SHA-512
6605a4b6b7ce8dc3f8c9bca5b1b5d5cd691e144934204e19c15431e3c31c1c89deb65c1cab7a3b038d6e9009c45449ed4499b83755424c98a215dacf769a39be
Type fulltextMimetype application/pdf

Authority records BETA

Hagenblad, AnnaGustafsson, FredrikKlein, Inger

Search in DiVA

By author/editor
Hagenblad, AnnaGustafsson, FredrikKlein, Inger
By organisation
Automatic ControlThe Institute of Technology
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 179 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

urn-nbn

Altmetric score

urn-nbn
Total: 455 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