Distributed neuro-fuzzy feature forecasting approach for condition monitoring
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
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.
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