Unsupervised electric motor fault detection by using deep autoencoders

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully...

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Bibliographic Details

Published in:	IEEE/CAA journal of automatica sinica Vol. 6; no. 2; pp. 441 - 451
Main Authors:	Principi, Emanuele, Rossetti, Damiano, Squartini, Stefano, Piazza, Francesco
Format:	Journal Article
Language:	English
Published:	Piscataway Chinese Association of Automation (CAA) 01.03.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accelerometers Algorithms Artificial neural networks Electric motors Fault detection Fault diagnosis Feature extraction Human performance Induction motors Multilayers Neural networks Signal processing Support vector machines Vibrations
ISSN:	2329-9266, 2329-9274
Online Access:	Get full text
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