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A Bayesian approach to fault isolation - Structure estimation and inference
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-1927-1690
2007 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This chapter considers a Bayesian inference method for fault isolation. Given a set of residuals, and a set of possible faults, the task is to calculate the probability distribution of the faults. The method requires conditional probability distribution of how the residuals respond given the possible faults. Especially important is to know the structure of this conditional probability distribution, since it facilitates the use of efficient Bayesian network techniques for the inference. The conditional probability distribution, and in particular its structure, is estimated from training data using a Bayesian approach. The approach is evaluated on a simple but illustrative example, where it is shown that the estimated structure and the distributions capture the dependencies that are important to make the correct isolation decisions.

Place, publisher, year, edition, pages
Elsevier, 2007. Vol. 1, 450-455 p.
Series
IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670 ; 1
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-154714DOI: 10.1016/B978-008044485-7/50076-2Scopus ID: 2-s2.0-84883918815ISBN: 9780080444857 (print)OAI: oai:DiVA.org:kth-154714DiVA: diva2:765823
Conference
6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2006; Beijing; China; 29 August 2006 through 1 September 2006; Code 85869
Note

QC 20141125

Available from: 2014-11-25 Created: 2014-10-27 Last updated: 2017-02-27Bibliographically approved

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Wahlberg, Bo

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CiteExportLink to record
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Citation style
  • apa
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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