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.…
Autor*innen: | Eva Viefhues, M. Döhler, Falk HilleORCiD, L. Mevel |
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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 |
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: | 10.1016/j.ifacol.2018.09.610 |
ISSN: | 2405-8963 |
Verfügbarkeit des Dokuments: | Datei 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 |