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

Published

December 6, 2023

Author(s)

Marc-Alexander Lutz, Bastian Schäfermeier, Rachael Sexton, Michael Sharp, Alden A. Dima, Stefan Faulstich, Jagan Mohini Aluri

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, making it challenging for decision-makers to use this information for optimizing operation and maintenance.To address this issue, this work presents three different approaches to calculate 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.The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information contained in the maintenance work orders. The resulting calculated reliability key performance indicator of the first approach are used as a benchmark for comparison with the results of the second and third approaches. The quality and time spent are considered as criteria for evaluation. Overall, these three methods 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.

Citation

Energies

Pub Type

Journals

Download Paper

https://doi.org/10.3390/en16247937

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Keywords

Wind Turbine, Operation and Maintenance, Key Performance Indicators, Technical Language Processing, Maintenance Work Orders, Reliability, Text Classification

Citation

Lutz, M. , Schäfermeier, B. , Sexton, R. , Sharp, M. , Dima, A. , Faulstich, S. and Aluri, J. (2023), KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines, Energies, [online], https://doi.org/10.3390/en16247937, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956375 (Accessed December 30, 2024)