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Data analytics for performance evaluation under uncertainties applied to an industrial refrigeration plant

Author
Cirera, J.; Cariño, J. A.; Zurita, D.; Ortega, J.A.
Type of activity
Journal article
Journal
IEEE access
Date of publication
2019-01-01
Volume
7
First page
64127
Last page
64135
DOI
10.1109/ACCESS.2019.2917079
Repository
http://hdl.handle.net/2117/165740 Open in new window
URL
https://ieeexplore.ieee.org/document/8715785 Open in new window
Abstract
Artificial intelligence has bounced into industrial applications contributing several advantages to the field and have led to the possibility to open new ways to solve many actual problems. In this paper, a data-driven performance evaluation methodology is presented and applied to an industrial refrigeration system. The strategy takes advantage of the Multivariate Kernel Density Estimation technique and Self-Organizing Maps to develop a robust method, which is able to determine a near-optimal pe...
Citation
Cirera, J. [et al.]. Data analytics for performance evaluation under uncertainties applied to an industrial refrigeration plant. "IEEE access", 1 Gener 2019, vol. 7, p. 64127-64135.
Keywords
Compressors, Kernel, Neurons, Performance evaluation, Refrigerants, Self-organizing feature maps, Uncertainty
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

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