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dc.contributor.author
Cosa Rodríguez, Pablo
dc.contributor.author
Martí Puig, Pere
dc.contributor.author
Caiafa, César Federico
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dc.contributor.author
Serra Serra, Moises
dc.contributor.author
Cusidó, Jordi
dc.contributor.author
Solé Casals, Jordi
dc.date.available
2023-11-14T12:54:29Z
dc.date.issued
2023-02
dc.identifier.citation
Cosa Rodríguez, Pablo; Martí Puig, Pere; Caiafa, César Federico; Serra Serra, Moises; Cusidó, Jordi; et al.; Exploratory Analysis of SCADA Data from Wind Turbines Using the K-Means Clustering Algorithm for Predictive Maintenance Purposes; MDPI; Machines; 11; 2; 2-2023; 1-15
dc.identifier.issn
2075-1702
dc.identifier.uri
http://hdl.handle.net/11336/218002
dc.description.abstract
Product maintenance costs throughout the product’s lifetime can account for between 30–60% of total operating costs, making it necessary to implement maintenance strategies. This problem not only affects the economy but is also related to the impact on the environment, since breakdowns are also responsible for the delivery of greenhouse gases. Industrial maintenance is a set of measures of a technical-organizational nature whose purpose is to sustain the functionality of theequipment and guarantee an optimal state of the machines over time, with the aim of saving costs, extending the useful life of the machines, saving energy, maximising production and availability, ensuring the quality of the product obtained, providing job security for technicians, preserving the environment, and reducing emissions as much as possible. Machine learning techniques can be used to detect or predict faults in wind turbines. However, labelled data suffers from many problems in this application because alarms are usually not clearly associated with a specific fault, some labelsare wrongly associated with a problem, and the imbalance between labels is evident. To avoid using labelled data, we investigate here the use of the clustering technique, more specifically K-means, and boxplot representations of the variables for a set of six different tests. Experimental results show that in some cases, the clustering and boxplot techniques allow us to determine outliers or identify erroneous behaviours of the wind turbines. These cases can then be investigated in detail by a specialist so that more efficient predictive maintenance can be carried out.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
MDPI
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
predictive maintenance
dc.subject
prognosis
dc.subject
machine learning
dc.subject
K-means
dc.subject.classification
Ingeniería del Petróleo, Energía y Combustibles
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dc.subject.classification
Ingeniería del Medio Ambiente
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Exploratory Analysis of SCADA Data from Wind Turbines Using the K-Means Clustering Algorithm for Predictive Maintenance Purposes
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-11-13T15:55:00Z
dc.journal.volume
11
dc.journal.number
2
dc.journal.pagination
1-15
dc.journal.pais
Suiza
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dc.description.fil
Fil: Cosa Rodríguez, Pablo. Open University of Catalonia. Faculty of Computer Science, Multimedia and Telecommunications.; España
dc.description.fil
Fil: Martí Puig, Pere. University of Vic-Central. Data and Signal Processing Group; España
dc.description.fil
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
dc.description.fil
Fil: Serra Serra, Moises. University of Vic-Central. Data and Signal Processing Group; España
dc.description.fil
Fil: Cusidó, Jordi. University of Vic-Central. Data and Signal Processing Group; España
dc.description.fil
Fil: Solé Casals, Jordi. University of Vic-Central. Data and Signal Processing Group; España
dc.journal.title
Machines
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2075-1702/11/2/270/htm
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/machines11020270
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