Stochastic Observability and Fault Diagnosis of Additive Changes in State Space Models
2002 (English)In: Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002, 2833-2836 vol.5 p.Conference paper (Refereed)
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.
Fault detection, Kalman filter, Stochastic observability
Engineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-91142DOI: 10.1109/ICASSP.2001.940236ISBN: 0-7803-7041-4OAI: oai:DiVA.org:liu-91142DiVA: diva2:617592
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Salt Lake City, UT, USA, May, 2001