Exploratory analysis of SCADA data from wind urbines using the K-Means clustering algorithm for predictive maintenance purposes
View/Open
Cita com:
hdl:2117/383657
Document typeArticle
Defense date2023-02-10
Rights accessOpen Access
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
:
Attribution 4.0 International
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 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.
CitationCosa, P. [et al.]. Exploratory analysis of SCADA data from wind urbines using the K-Means clustering algorithm for predictive maintenance purposes. "Machines", 10 Febrer 2023, vol. 11, núm. 2, article 270.
ISSN2075-1702
Publisher versionhttps://www.mdpi.com/2075-1702/11/2/270
Files | Description | Size | Format | View |
---|---|---|---|---|
machines-11-00270.pdf | 4,300Mb | View/Open |