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Article: Robust recursive eigendecomposition and subspace-based algorithms with application to fault detection in wireless sensor networks

TitleRobust recursive eigendecomposition and subspace-based algorithms with application to fault detection in wireless sensor networks
Authors
KeywordsFault Detection
Orthonormal Projection Approximation Subspace Tracking (Past) (Opast)
Outlier Detection
Past Algorithm With Deflation (Pastd)
Recursive Principal Component Analysis (R-Pca)
Robust Statistics
Subspace Eigendecomposition (Ed)
Wireless Sensor Networks (Wsns)
Issue Date2012
PublisherIEEE
Citation
IEEE Transactions on Instrumentation and Measurement, 2012, v. 61 n. 6, p. 1703-1718 How to Cite?
AbstractThe principal component analysis (PCA) is a valuable tool in multivariate statistics, and it is an effective method for fault detection in wireless sensor networks (WSNs) and other related applications. However, its online implementation requires the computation of eigendecomposition (ED) or singular value decomposition. To reduce the arithmetic complexity, we propose an efficient fault detection approach using the subspace tracking concept. In particular, two new robust subspace tracking algorithms are developed, namely, the robust orthonormal projection approximation subspace tracking (OPAST) with rank-1 modification and the robust OPAST with deflation. Both methods rely on robust M-estimate-based recursive covariance estimate to improve the robustness against the effect of faulty samples, and they offer different tradeoff between fault detection accuracy and arithmetic complexity. Since only the ED in the major subspace is computed, their arithmetic complexities are much lower than those of other conventional PCA-based algorithms. Furthermore, we propose new robust T 2 score and SPE detection criteria with recursive update formulas to improve the robustness over their conventional counterparts and to facilitate online implementation for the proposed robust subspace ED and tracking algorithms. Computer simulation and experimental results on WSN data show that the proposed fault detection approach, which combines the aforementioned robust subspace tracking algorithms with the robust detection criteria, is able to achieve better performance than other conventional approaches. Hence, it serves as an attractive alternative to other conventional approaches to fault detection in WSNs and other related applications because of its low complexity, efficient recursive implementation, and good performance. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/155760
ISSN
2015 Impact Factor: 1.808
2015 SCImago Journal Rankings: 1.022
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_US
dc.contributor.authorWu, HCen_US
dc.contributor.authorTsui, KMen_US
dc.date.accessioned2012-08-08T08:35:12Z-
dc.date.available2012-08-08T08:35:12Z-
dc.date.issued2012en_US
dc.identifier.citationIEEE Transactions on Instrumentation and Measurement, 2012, v. 61 n. 6, p. 1703-1718en_US
dc.identifier.issn0018-9456en_US
dc.identifier.urihttp://hdl.handle.net/10722/155760-
dc.description.abstractThe principal component analysis (PCA) is a valuable tool in multivariate statistics, and it is an effective method for fault detection in wireless sensor networks (WSNs) and other related applications. However, its online implementation requires the computation of eigendecomposition (ED) or singular value decomposition. To reduce the arithmetic complexity, we propose an efficient fault detection approach using the subspace tracking concept. In particular, two new robust subspace tracking algorithms are developed, namely, the robust orthonormal projection approximation subspace tracking (OPAST) with rank-1 modification and the robust OPAST with deflation. Both methods rely on robust M-estimate-based recursive covariance estimate to improve the robustness against the effect of faulty samples, and they offer different tradeoff between fault detection accuracy and arithmetic complexity. Since only the ED in the major subspace is computed, their arithmetic complexities are much lower than those of other conventional PCA-based algorithms. Furthermore, we propose new robust T 2 score and SPE detection criteria with recursive update formulas to improve the robustness over their conventional counterparts and to facilitate online implementation for the proposed robust subspace ED and tracking algorithms. Computer simulation and experimental results on WSN data show that the proposed fault detection approach, which combines the aforementioned robust subspace tracking algorithms with the robust detection criteria, is able to achieve better performance than other conventional approaches. Hence, it serves as an attractive alternative to other conventional approaches to fault detection in WSNs and other related applications because of its low complexity, efficient recursive implementation, and good performance. © 2012 IEEE.en_US
dc.languageengen_US
dc.publisherIEEE-
dc.relation.ispartofIEEE Transactions on Instrumentation and Measurementen_US
dc.rightsIEEE Transactions on Instrumentation and Measurement. Copyright © IEEE-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectFault Detectionen_US
dc.subjectOrthonormal Projection Approximation Subspace Tracking (Past) (Opast)en_US
dc.subjectOutlier Detectionen_US
dc.subjectPast Algorithm With Deflation (Pastd)en_US
dc.subjectRecursive Principal Component Analysis (R-Pca)en_US
dc.subjectRobust Statisticsen_US
dc.subjectSubspace Eigendecomposition (Ed)en_US
dc.subjectWireless Sensor Networks (Wsns)en_US
dc.titleRobust recursive eigendecomposition and subspace-based algorithms with application to fault detection in wireless sensor networksen_US
dc.typeArticleen_US
dc.identifier.emailChan, SC:scchan@eee.hku.hken_US
dc.identifier.authorityChan, SC=rp00094en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1109/TIM.2012.2186654en_US
dc.identifier.scopuseid_2-s2.0-84862818453-
dc.identifier.hkuros207543-
dc.identifier.hkuros211931-
dc.identifier.hkuros208539-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84861187001&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume61en_US
dc.identifier.issue6en_US
dc.identifier.spage1703en_US
dc.identifier.epage1718en_US
dc.identifier.isiWOS:000304092700016-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridChan, SC=13310287100en_US
dc.identifier.scopusauthoridWu, HC=55003800800en_US
dc.identifier.scopusauthoridTsui, KM=55004669500en_US

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