Enhanced auto-associative neural networks for sensor diagnostics (E-AANN)

We address the problem of sensor fault diagnosis in complex systems. The motivation for this work is the common problem encountered in industrial setting, i.e. sensor shift, drift and outright failure. The approach proposed in this paper is based on auto-associative neural networks but has been exte...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Najafi, M., Gulp, C., Langari, R.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We address the problem of sensor fault diagnosis in complex systems. The motivation for this work is the common problem encountered in industrial setting, i.e. sensor shift, drift and outright failure. The approach proposed in this paper is based on auto-associative neural networks but has been extended to address some intrinsic deficiencies of these types of networks in practical setting. In particular, it is shown that the proposed approach provides the basic functionality needed for single sensor fault detection in a multi-sensor environment with limited additional computational burden. This work is presently under further development to address multi-sensor failures.
ISSN:1098-7584
DOI:10.1109/FUZZY.2004.1375771