Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant
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 refr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8715785/ |
| _version_ | 1819276263442874368 |
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| author | Josep Cirera Jesus A. Carino Daniel Zurita Juan A. Ortega |
| author_facet | Josep Cirera Jesus A. Carino Daniel Zurita Juan A. Ortega |
| author_sort | Josep Cirera |
| collection | DOAJ |
| description | 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 performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows comparing the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method. |
| first_indexed | 2024-12-23T23:37:26Z |
| format | Article |
| id | doaj.art-3f9de6fcf887466f91193e4ba1607389 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:37:26Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3f9de6fcf887466f91193e4ba16073892022-12-21T17:25:50ZengIEEEIEEE Access2169-35362019-01-017641276413510.1109/ACCESS.2019.29170798715785Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration PlantJosep Cirera0https://orcid.org/0000-0002-7043-4643Jesus A. Carino1https://orcid.org/0000-0003-4069-3561Daniel Zurita2https://orcid.org/0000-0001-6388-7559Juan A. Ortega3https://orcid.org/0000-0002-1403-8152MCIA Research Center, Technical University of Catalonia (UPC), Terrassa, SpainMCIA Research Center, Technical University of Catalonia (UPC), Terrassa, SpainMCIA Research Center, Technical University of Catalonia (UPC), Terrassa, SpainMCIA Research Center, Technical University of Catalonia (UPC), Terrassa, SpainArtificial 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 performance map, taking into account the system uncertainties and the multiple signals involved in the process. A normality model is used to detect and filter non-representative operating samples to subsequently develop a reliable performance map. The performance map allows comparing the plant assessment under the same operating conditions and permits to identify the potential system improvement capabilities. To ensure that the resulting evaluation is trustworthy, a robustness strategy is developed to identify either possible new operation conditions or abnormal situations in order to avoid uncertain assessments. Furthermore, the proposed approach is tested with real industrial plant data to validate the suitability of the method.https://ieeexplore.ieee.org/document/8715785/Artificial intelligencecompression refrigerationself-organizing mapsuncertainty |
| spellingShingle | Josep Cirera Jesus A. Carino Daniel Zurita Juan A. Ortega Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant IEEE Access Artificial intelligence compression refrigeration self-organizing maps uncertainty |
| title | Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant |
| title_full | Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant |
| title_fullStr | Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant |
| title_full_unstemmed | Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant |
| title_short | Data Analytics for Performance Evaluation Under Uncertainties Applied to an Industrial Refrigeration Plant |
| title_sort | data analytics for performance evaluation under uncertainties applied to an industrial refrigeration plant |
| topic | Artificial intelligence compression refrigeration self-organizing maps uncertainty |
| url | https://ieeexplore.ieee.org/document/8715785/ |
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