Comparison of different noise analysis methods for error detection on induction machines

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autorinnen und Autoren: Stiller M, Wagner J, Thyroff D, Hahn I
Herausgeber: IEEE
Jahr der Veröffentlichung: 2017
Tagungsband: 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)
Seitenbereich: 110-116
ISBN: 978-1-5090-0409-6
Sprache: Englisch


Abstract


Increasing levels of noise and vibration are early indicators for an evoluting error or defect on an electrical machine. There are many different approaches in monitoring the health of an electrical machine, e.g. noise-, vibration- or current-based methods. This paper investigates and compares different methods for noise analysis. Each method provides an own representation of the analysed signal. Therefore, the presented methods are compared regarding their suitability in analysing electrical machines. In the following sections, first the theory behind all methods is presented. Later on, selected error states are re-enacted using an induction machine. Finally, utilizing measured noise and acoustic signals allows assessing the presented methods.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Hahn, Ingo Prof. Dr.-Ing.
Professur für Elektrische Antriebe und Maschinen
Stiller, Matthias
Professur für Elektrische Antriebe und Maschinen
Thyroff, Dominik
Professur für Elektrische Antriebe und Maschinen
Wagner, Johannes
Professur für Elektrische Antriebe und Maschinen


Zitierweisen

APA:
Stiller, M., Wagner, J., Thyroff, D., & Hahn, I. (2017). Comparison of different noise analysis methods for error detection on induction machines. In IEEE (Eds.), 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) (pp. 110-116). Tinos, GR.

MLA:
Stiller, Matthias, et al. "Comparison of different noise analysis methods for error detection on induction machines." Proceedings of the 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Tinos Ed. IEEE, 2017. 110-116.

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Zuletzt aktualisiert 2018-21-10 um 00:10