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Distributed neuro-fuzzy feature forecasting approach for condition monitoring

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10.1109/ETFA.2014.7005180
 
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hdl:2117/26982

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Zurita Millán, DanielMés informacióMés informació
Cariño Corrales, Jesús AdolfoMés informació
Delgado Prieto, MiquelMés informacióMés informacióMés informació
Ortega Redondo, Juan AntonioMés informacióMés informacióMés informació
Document typeConference report
Defense date2014
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
The industrial machinery reliability represents a critical factor in order to assure the proper operation of the whole productive process. In regard with this, diagnosis schemes based on physical magnitudes acquisition, features calculation, features reduction and classification are being applied. However, in this paper, in order to enhance the condition monitoring capabilities, a forecasting approach is proposed, in which not only the current status of the system under monitoring in identified, diagnosis, but also the future condition is assessed, prognosis. The novelties of the proposed methodology are based on a distributed features forecasting approach by means of adaptive neuro-fuzzy inference system models. The proposed method is validated by means of an accelerated bearing degradation experimental platform.
CitationZurita, D. [et al.]. Distributed neuro-fuzzy feature forecasting approach for condition monitoring. A: IEEE International Conference on Emerging Technologies and Factory Automation. "Proceedings of the 19th IEEE International Conference on Emerging Technologies and Factory Automation". Barcelona: Institute of Electrical and Electronics Engineers (IEEE), 2014. 
URIhttp://hdl.handle.net/2117/26982
DOI10.1109/ETFA.2014.7005180
ISBN978-1-4799-4846-8
Publisher versionhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7005180&isnumber=7005023
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  • Departament d'Enginyeria Electrònica - Ponències/Comunicacions de congressos [1.608]
  • MCIA - Motion Control and Industrial Applications Research Group - Ponències/Comunicacions de congressos [131]
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