  Maintenance work orders are commonly used to document information about wind
turbine operation and maintenance. This includes details about proactive and
reactive wind turbine downtimes, such as preventative and corrective
maintenance. However, the information contained in maintenance work orders is
often unstructured and difficult to analyze, presenting challenges for
decision-makers wishing to use it for optimizing operation and maintenance. To
address this issue, this work compares three different approaches to calculate
reliability by performance indicators from maintenance work orders. The first
approach involves manual labeling of the maintenance work orders by domain
experts, using the schema defined in an industrial guideline to assign the
label accordingly. The second approach involves the development of a model that
automatically labels the maintenance work orders using text classification
methods. Through this method, we are able to achieve macro average and weighted
average F1-Scores of 0.75 and 0.85 respectively. The third technique uses an
AI-assisted tagging tool to tag and structure the raw maintenance information,
together with a novel rule-based approach for extracting relevant maintenance
work orders for failure rate calculation. In our experiments the AI-assisted
tool leads to a 88% drop in tagging time in comparison to the other two
approaches, while expert labeling and text classification are more accurate in
KPI extraction. Overall, our findings make extracting maintenance information
from maintenance work orders more efficient, enable the assessment of
reliability key performance indicators and therefore support the optimization
of wind turbine operation and maintenance.
