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Asymptotic analysis of subspace-based data-driven residual for fault detection with uncertain reference

  • The local asymptotic approach is promising for vibration-based fault diagnosis when associated to a subspace-based residual function and efficient hypothesis testing tools. It has the ability of detecting small changes in some chosen system parameters. In the residual function,the left null space of the observability matrix associated to a reference model is confronted to the Hankel matrix of output covariances estimated from test data. When this left null space is not perfectly known from a model, it should be replaced by an estimate from data to avoid model errors in the residual computation. In this paper, the asymptotic distribution of the resulting data-driven residual is analyzed and its covariance is estimated, which includes also the covariance related to the reference null space estimate. The advantages of the data-driven residual are demonstrated in a numerical study, and the importance of including the covariance of the reference null space estimate is shown, which increasesThe local asymptotic approach is promising for vibration-based fault diagnosis when associated to a subspace-based residual function and efficient hypothesis testing tools. It has the ability of detecting small changes in some chosen system parameters. In the residual function,the left null space of the observability matrix associated to a reference model is confronted to the Hankel matrix of output covariances estimated from test data. When this left null space is not perfectly known from a model, it should be replaced by an estimate from data to avoid model errors in the residual computation. In this paper, the asymptotic distribution of the resulting data-driven residual is analyzed and its covariance is estimated, which includes also the covariance related to the reference null space estimate. The advantages of the data-driven residual are demonstrated in a numerical study, and the importance of including the covariance of the reference null space estimate is shown, which increases the detection Performance.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Eva Viefhues, M. Döhler, Falk Hille, L. Mevel
Persönliche Herausgeber/innen:S. Simani, K. Patan
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2018
Jahr der Erstveröffentlichung:2018
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.2 Ingenieurbau
Herausgeber (Institution):International Federation of Automatic Control
Verlag:Elsevier
Jahrgang/Band:51
Ausgabe/Heft:24
Erste Seite:414
Letzte Seite:419
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Fault detection; Residual evaluation; Statistical tests; Subspace-based method; Uncertainty in reference; Vibration measurements
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Infrastruktur / Sicherheit und Lebensdauer von Bauwerken
Veranstaltung:10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2018
Veranstaltungsort:Warsaw, Poland
Beginndatum der Veranstaltung:29.08.2018
Enddatum der Veranstaltung:31.08.2018
DOI:https://doi.org/10.1016/j.ifacol.2018.09.610
URL:http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000447016900061
ISSN:2405-8963
Verfügbarkeit des Volltexts:Volltext-PDF im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:17.10.2018
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:01.11.2018