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Statistical Signal Processing Approaches to Fault Detection
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2007 (English)In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 31, no 1, 41-54 p.Article in journal (Refereed) Published
Abstract [en]

The parity space approach to fault detection and isolation (FDI) has been developed during the last 20 years, and the focus here is to describe its application to stochastic systems. A mixed model with both stochastic inputs and deterministic disturbances and faults is formulated over a sliding window. Algorithms for detecting and isolating faults on-line and analyzing the probability for correct and incorrect decisions off-line are provided. A major part of the paper is devoted to discussing properties of this model-based approach and generalizations to cases of incomplete model knowledge, and non-linear non-Gaussian models. For this purpose, a simulation example is used throughout the paper for numerical illustrations, and real-life applications for motivations. The final section discusses the reverse problem: fault detection approaches to statistical signal processing. It is motivated by three applications that a simple CUSUM detector in feedback loop with an adaptive filter can mitigate the inherent trade-off between estimation accuracy and tracking speed in linear filters.

Place, publisher, year, edition, pages
Elsevier, 2007. Vol. 31, no 1, 41-54 p.
Keyword [en]
Adaptive filters, Diagnosis, Fault detection, Kalman filtering, Linear systems, Principal component analysis, Subspace identification
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-49688DOI: 10.1016/j.arcontrol.2007.02.004OAI: diva2:270584

© 2007 Elsevier Ltd. All rights reserved.

Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-07-23

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ReferencesLink to record
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