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Robust Residual Selection for Fault Detection
Vanderbilt University, TN 37235 USA.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0808-052X
Vanderbilt University, TN 37235 USA.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
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2014 (English)In: 2014 IEEE 53RD ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2014, 5764-5769 p.Conference paper, Published paper (Refereed)
Abstract [en]

A number of residual generation methods have been developed for robust model-based fault detection and isolation (FDI). There have also been a number of offline (i.e., design-time) methods that focus on optimizing FDI performance (e.g., trading off detection performance versus cost). However, design-time algorithms are not tuned to optimize performance for different operating regions of system behavior. To do this, would need to define online measures of sensitivity and robustness, and use them to select the best residual set online as system behavior transitions between operating regions. In this paper we develop a quantitative measure of residual performance, called the detectability ratio that applies to additive and multiplicative uncertainties when determining the best residual set in different operating regions. We discuss this methodology and demonstrate its effectiveness using a case study.

Place, publisher, year, edition, pages
IEEE , 2014. 5764-5769 p.
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-137419ISI: 000370073805149ISBN: 978-1-4673-6090-6 (print)OAI: oai:DiVA.org:liu-137419DiVA: diva2:1095567
Conference
IEEE 53rd Annual Conference on Decision and Control (CDC)
Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2017-05-15

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