Open this publication in new window or tab >>Show others...
2018 (English)In: Proceedings: 2018 XIII International Conference on Electrical Machines (ICEM), IEEE, 2018, p. 1754-1760Conference paper, Published paper (Refereed)
Abstract [en]
This paper proposes an automatic method, for monitoring inverter fed induction motors using external stray flux measurements. The method relies on the marginal power spectrum of the Synchrosqueezed Wavelet Transform for the feature extraction stage and on Principal Component Analysis for the reduction of the high dimensionality of the generated feature vector. For the next stage two approaches were tested: a) a fault detector based on a one-class classifier and b) a fault diagnosis module based on a multiclass classifier. Both of them achieve high accuracies when tested with measurements coming from an experimental set up able to simulate stator short circuits and bearing faults. An explanation of the performance is given by visual inspection of the projection of the feature vectors into a three-dimensional space.
Place, publisher, year, edition, pages
IEEE, 2018
Series
International Conference on Electrical Machines, ICEM, ISSN 2381-4802
Keywords
Fault detection, Fault diagnosis, Induction Motors, Inverter Fed Motors
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-72868 (URN)10.1109/ICELMACH.2018.8506962 (DOI)000542969300259 ()2-s2.0-85057213032 (Scopus ID)
Conference
2018 XIII International Conference on Electrical Machines (ICEM), 3-6 September, 2018, Alexandroupoli, Greece
Note
ISBN för värdpublikation: 978-1-5386-2477-7, 978-1-5386-2478-4
2019-02-122019-02-122020-09-23Bibliographically approved