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Analysis of machine learning based condition monitoring schemes applied to complex electromechanical systems

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10.1109/ETFA46521.2020.9212026
 
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hdl:2117/330308

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Arellano Espitia, FranciscoMés informacióMés informacióMés informació
González Abreu, Artvin Darién
Delgado Prieto, MiquelMés informacióMés informacióMés informació
Saucedo Dorantes, Juan Jose
Osornio Rios, Roque A.
Document typeConference report
Defense date2020
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
In the modern industry framework, the application of condition monitoring schemes over electromechanical systems is being subjected to demanding requirements. Currently, the massive digitalization of industrial assets allows the investigation towards multiple monitoring strategies capable of emphasize deviations over the nominal system operation. However, the most prominent techniques, such as Machine Learning, present great challenges in complex systems. In this regard, the proposed study presents the analysis of the diagnostic capabilities resulting from the classical approaches based on machine learning facing to complex electromechanical systems that implies a working environment subject to different operation condition, configurations with multiple components and the presence of faults of different nature (mechanical, electrical, electromagnetic), under isolated or combined scenarios. Discriminative feature extraction capabilities and classification accuracy will be analyzed as performance measures.
CitationArellano, F. [et al.]. Analysis of machine learning based condition monitoring schemes applied to complex electromechanical systems. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1419-1422. ISBN 978-1-7281-8957-4. DOI 10.1109/ETFA46521.2020.9212026. 
URIhttp://hdl.handle.net/2117/330308
DOI10.1109/ETFA46521.2020.9212026
ISBN978-1-7281-8957-4
Publisher versionhttps://ieeexplore.ieee.org/abstract/document/9212026
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  • Doctorat en Enginyeria Electrònica - Ponències/Comunicacions de congressos [86]
  • Departament d'Enginyeria Electrònica - Ponències/Comunicacions de congressos [1.665]
  • MCIA - Motion Control and Industrial Applications Research Group - Ponències/Comunicacions de congressos [131]
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