Exploratory analysis of SCADA data fromwind turbines using the K-means clustering algorithm for predictive maintenance purposes
| Autor: | Pablo Cosa Rodriguez, Pere Marti-Puig, Cesar F. Caiafa, Moisès Serra-Serra, Jordi Cusidó, Jordi Solé-Casals |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Projectes i de la Construcció |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Informática
SCADA data Control and Optimization Mechanical Engineering Ingeniería Renewable energies Predictive maintenance Failure analysis (Engineering) Prognosis Industrial and Manufacturing Engineering Clustering Aerogeneradors--Manteniment Control and Systems Engineering Wind turbines--Maintenance Aprenentatge automàtic Machine learning Computer Science (miscellaneous) Enginyeria mecànica::Mecànica [Àrees temàtiques de la UPC] Anàlisi de fallades (Enginyeria) Electrical and Electronic Engineering Energies::Energia eòlica::Aerogeneradors [Àrees temàtiques de la UPC] K-means Wind turbine |
| Zdroj: | SEDICI (UNLP) Universidad Nacional de La Plata instacron:UNLP Machines Volume 11 Issue 2 Pages: 270 |
| Popis: | 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 the equipment 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 labels are 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. Instituto Argentino de Radioastronomía |
| Databáze: | OpenAIRE |
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