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Residual change detection using low-complexity sequential quantile estimation
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-0808-052X
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.
2017 (English)In: IFAC World Congress, 2017Conference paper, Published paper (Other academic)
Abstract [en]

Detecting changes in residuals is important for fault detection and is commonly performed by thresholding the residual using, for example, a CUSUM test. However, detecting variations in the residual distribution, not causing a change of bias or increased variance, is difficult using these methods. A plug-and-play residual change detection approach is proposed based on sequential quantile estimation to detect changes in the residual cumulative density function. An advantage of the proposed algorithm is that it is non-parametric and has low computational cost and memory usage which makes it suitable for on-line implementations where computational power is limited.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Diagnosis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-138369OAI: oai:DiVA.org:liu-138369DiVA: diva2:1109456
Conference
IFAC 2017 World Congress, Toulouse, France The 20th World Congress of the International Federation of Automatic Control, 9-14 July 2017
Note

Proceedings ännu ej utgivet

Available from: 2017-06-14 Created: 2017-06-14 Last updated: 2017-06-20

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CiteExportLink to record
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Citation style
  • apa
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