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A Data-Driven and Probabilistic Approach to Residual Evaluation for Fault Diagnosis
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
2011 (English)In: 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 2011, Institute of Electrical and Electronics Engineers (IEEE), 2011, 95-102 p.Conference paper (Refereed)
Abstract [en]

An important step in fault detection and isolation is residual evaluation where residuals, signals ideally zero in the no-fault case, are evaluated with the aim to detect changes in their behavior caused by faults. Generally, residuals deviate from zero even in the no-fault case and their probability distributions exhibit non-stationary features due to, e.g., modeling errors, measurement noise, and different operating conditions. To handle these issues, this paper proposes a data-driven approach to residual evaluation based on an explicit comparison of the residual distribution estimated on-line and a no-fault distribution, estimated off-line using training data. The comparison is done within the framework of statistical hypothesis testing. With the Generalized Likelihood Ratio test statistic as starting point, a more powerful and computational efficient test statistic is derived by a properly chosen approximation to one of the emerging likelihood maximization problems. The proposed approach is evaluated with measurement data on a residual for diagnosis of the gas-flow system of a Scania truck diesel engine. The proposed test statistic performs well, small faults can for example be reliable detected in cases where regular methods based on constant thresholding fail.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2011. 95-102 p.
Series
Decision and Control (CDC), ISSN 0191-2216, E-ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-137787DOI: 10.1109/CDC.2011.6160714ScopusID: 2-s2.0-84860683768ISBN: 978-1-61284-800-6 (print)ISBN: 978-1-61284-801-3 (electronic)ISBN: 978-1-4673-0457-3 (electronic)ISBN: 978-1-61284-799-3 (electronic)OAI: oai:DiVA.org:liu-137787DiVA: diva2:1102622
Conference
50th IEEE Conference on Decision and Control, 12-15 December 2011, Orlando, Florida, USA.
Available from: 2017-05-29 Created: 2017-05-29 Last updated: 2017-06-09Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
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More languages
Output format
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