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Multimodal forecasting methodology applied to industrial process monitoring

Author
Zurita, D.; Delgado Prieto, M.; Cariño , J.A.; Ortega, J.A.
Type of activity
Journal article
Journal
IEEE transactions on industrial informatics
Date of publication
2017-09-20
First page
1
Last page
10
DOI
https://doi.org/10.1109/TII.2017.2755099 Open in new window
Repository
http://hdl.handle.net/2117/109279 Open in new window
URL
http://ieeexplore.ieee.org/document/8048029/ Open in new window
Abstract
IEEE Industrial process modelling represents a key factor to allow the future generation of industrial manufacturing plants. In this regard, accurate models of critical signals need to be designed in order to forecast process deviations. In this work a novel multimodal forecasting methodology based on adaptive dynamics packaging and codification of the process operation is proposed. First, a target signal is decomposed by means of the Empirical Mode Decomposition in order to identify the charact...
Citation
Zurita, D., Delgado Prieto, M., Cariño , J.A., Ortega, J.A. Multimodal forecasting methodology applied to industrial process monitoring. "IEEE transactions on industrial informatics", 20 Setembre 2017, p. 1-10.
Keywords
Adaptation models, Biological system modeling, Computational modeling, Forecasting, Fuzzy neural networks, Industrial plants, Packaging, Predictive models, Time series analysis
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

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