Abstract
Abstract Maintenance data of wind turbines is an important information source for calculating key performance indicators. Also, it can be used for developing models for early fault detection. Both activities aim for supporting informed decisions in operation and maintenance. However, such data is rarely available in a structured and standardized format which hinders the interoperability of different enterprises. Consequently, maintenance information is often unused or only usable with considerable personnel effort. To digitalize wind farm maintenance, a digitalization workflow is developed and presented in this paper. The workflow consists of the steps optical character recognition, information extraction and text classification. The workflow is applied on real-world wind turbine service reports and invoices. First results for each step show good performance metrics and potential for further real-world application of the proposed method.
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