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

Marc-Alexander Lutz, Bastian Schäfermeier (), Rachael Sexton, Michael Sharp, Alden Dima, Stefan Faulstich and Jagan Mohini Aluri
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Marc-Alexander Lutz: Fraunhofer Institute for Energy Economics and Energy System Technology, Joseph-Beuys-Straße 8, 34117 Kassel, Germany
Bastian Schäfermeier: Fraunhofer Institute for Energy Economics and Energy System Technology, Joseph-Beuys-Straße 8, 34117 Kassel, Germany
Rachael Sexton: National Institute for Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
Michael Sharp: National Institute for Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
Alden Dima: National Institute for Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
Stefan Faulstich: Fraunhofer Institute for Energy Economics and Energy System Technology, Joseph-Beuys-Straße 8, 34117 Kassel, Germany
Jagan Mohini Aluri: Fraunhofer Institute for Energy Economics and Energy System Technology, Joseph-Beuys-Straße 8, 34117 Kassel, Germany

Energies, 2023, vol. 16, issue 24, 1-14

Abstract: 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 calculating reliability key 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 F 1 -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 an 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.

Keywords: wind turbine; operation and maintenance; key performance indicators; technical language processing; maintenance work orders; reliability; text classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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