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Autoencoder based feature reduction analysis applied to electromechanical systems condition monitoring

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10.1109/ETFA.2019.8869371
 
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hdl:2117/175570

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Arellano Espitia, FranciscoMés informacióMés informacióMés informació
Saucedo Dorantes, Juan Jose
Delgado Prieto, MiquelMés informacióMés informacióMés informació
Osornio Rios, Roque A.
Document typeConference report
Defense date2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
Condition monitoring in electromechanical systems represents, currently, one of the most critical challenges dealing with the advancement and modernization in intelligent manufacturing. In this regard, machine learning based algorithms widely applied in other technological fields are being considered now to face the automatic feature extraction on the electric machine monitoring. In this study, a monitoring scheme is considered for faults detection performance evaluation, where vibrations signal under different fault conditions are acquired. Thus, the common electric machine monitoring framework, that is, a set of features estimated from a limited number of measurements, is considered in front of the three main dimensionality reduction approaches: principal component analysis, linear discriminant analysis and auto-encoder based. Performance of the corresponding approaches are studied and discussed experimentally. It is revealed that, although scheme based on auto-encoder provides enhanced diagnosis results, it is still necessary to carry out a detailed study on the automatic extraction capabilities of important features for the detection of faults.
CitationArellano, F. [et al.]. Autoencoder based feature reduction analysis applied to electromechanical systems condition monitoring. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): proceedings: University of Zaragoza, Zaragoza, Spain: 10-13 September, 2019". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 891-897. 
URIhttp://hdl.handle.net/2117/175570
DOI10.1109/ETFA.2019.8869371
ISBN978-1-7281-0303-7
Publisher versionhttps://ieeexplore.ieee.org/document/8869371
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  • Doctorat en Enginyeria Electrònica - Ponències/Comunicacions de congressos [78]
  • Departament d'Enginyeria Electrònica - Ponències/Comunicacions de congressos [1.626]
  • MCIA - Motion Control and Industrial Applications Research Group - Ponències/Comunicacions de congressos [131]
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