A data-based model for condition monitoring and maintenance planning for protective coating systems for wind tower structures

Andreas W. Momber, Tim W. Nattkemper, Daniel Langenkämper, Torben Möller, Daniel Brün, Peter Schaumann and Sulaiman Shojai

Renewable Energy, 2022, vol. 186, issue C, 957-973

Abstract: The application of protective coating systems is the major measure against the corrosion of steel structures for onshore wind turbines. The organic coatings are, however, susceptible to atmospheric exposure and tend to deteriorate during the operation. At the same time, onshore turbines become more powerful and require taller and more resistant tower structures. The inspection and condition monitoring of protective coating systems on large onshore turbines (in excess of 120 m height) is a demanding and time-consuming procedure and requires high human effort. The rapid developments in digitization and data analysis offer opportunities to notably increase the efficiency of monitoring processes and to develop (semi-)automated standardized procedures. The paper describes a data-oriented approach to utilize digital data for the monitoring and maintenance planning of surface protection systems of large onshore wind turbines. The proposed approach includes the following steps: the segmentation of an existing wind power structure into a number of reference areas based on an In-situ Virtual Twin; the definition of a local deterioration degree for each individual reference area; the annotation of image data; the use of heterogenous multi-modal data (image data, geodetical data, meteorological data, profile scanning data) as the sources for condition assessment and monitoring. An example procedure is exercised for a tower structure of an onshore wind power turbine in order to illustrate the practical relevance of the approach.

Keywords: Coatings; Computer vision; Corrosion; Image processing; Maintenance optimization; Wind turbines; Virtual twin (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:186:y:2022:i:c:p:957-973

DOI: 10.1016/j.renene.2022.01.022

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