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The application of protective coating systems is the major measure against the corrosion of stationary marine steel structures. During their service, protective coatings are exposed to various loads, and their protectiveness deteriorates. Digital visual inspection tools are becoming a standard for the condition monitoring of coatings for large and complex structures. One of the challenges with the use of these tools is the accumulation of large amounts of visual data to be evaluated and assessed in short time intervals. A comprehensive interpretation of such image collections requires detection, classification, and quantification processes, which are usually performed by domain experts (coating inspectors). However, the data volume and the rich content of the images make the support by software tools inevitable. This contribution defines requirements for image data annotation, and it presents for the first time the application of an online exploration and annotation tool to coating condition monitoring data and its introduction into a prescriptive coating maintenance model. The software architecture of the system is described, special features are illustrated with different use-cases, and future developments are discussed. The utilization of machine learning-based assessment tools is presented. • First integrative data-driven approach for online analysis of large volumes of visual monitoring data. • Integration of visual data into a prescriptive maintenance model. • Coating deterioration modeling based on pixel footprints of images. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]