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Stochastic Observability and Fault Diagnosis of Additive Changes in State Space Models
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2002 (English)In: Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002, 2833-2836 vol.5 p.Conference paper, Published paper (Refereed)
Abstract [en]

We derive a Kalman filter based on data from a sliding window. This is used for a new approach to fault detection and diagnosis, where the state estimate from past data is compared to the state estimate of some of the future data. We suggest a method to judge the quality of diagnosis in a simple way. For fault estimation in the diagnosis, the general concept of stochastic observability in linear systems is introduced. Its role on the design step is illustrated on a problem of estimating the true velocity of a car.

Place, publisher, year, edition, pages
2002. 2833-2836 vol.5 p.
Keyword [en]
Fault detection, Kalman filter, Stochastic observability
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-91142DOI: 10.1109/ICASSP.2001.940236ISBN: 0-7803-7041-4 (print)OAI: oai:DiVA.org:liu-91142DiVA: diva2:617592
Conference
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, UT, USA, May, 2001
Available from: 2013-04-23 Created: 2013-04-17 Last updated: 2013-04-23

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Gustafsson, Fredrik

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  • apa
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
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