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Maintenance work orders are commonly used to document information about windturbine operation and maintenance. This includes details about proactive andreactive wind turbine downtimes, such as preventative and correctivemaintenance. However, the information contained in maintenance work orders isoften unstructured and difficult to analyze, presenting challenges fordecision-makers wishing to use it for optimizing operation and maintenance. Toaddress this issue, this work compares three different approaches to calculatereliability by performance indicators from maintenance work orders. The firstapproach involves manual labeling of the maintenance work orders by domainexperts, using the schema defined in an industrial guideline to assign thelabel accordingly. The second approach involves the development of a model thatautomatically labels the maintenance work orders using text classificationmethods. Through this method, we are able to achieve macro average and weightedaverage F1-Scores of 0.75 and 0.85 respectively. The third technique uses anAI-assisted tagging tool to tag and structure the raw maintenance information,together with a novel rule-based approach for extracting relevant maintenancework orders for failure rate calculation. In our experiments the AI-assistedtool leads to a 88% drop in tagging time in comparison to the other twoapproaches, while expert labeling and text classification are more accurate inKPI extraction. Overall, our findings make extracting maintenance informationfrom maintenance work orders more efficient, enable the assessment ofreliability key performance indicators and therefore support the optimizationof wind turbine operation and maintenance.

energies

KPI Extraction from Maintenance Work Orders -- A Comparison of Expert  Labeling, Text Classification and AI-Assisted Tagging for Computing Failure  Rates of Wind Turbines