Digitalization Workflow for Automated Structuring and Standardization of Maintenance Information of Wind Turbines into Domain Standard as a Basis for Reliability KPI Calculation

Lutz, Marc‐Alexander; Walgern, Julia; Beckh, Katharina; Schneider, J; Faulstich, Stefan; Pfaffel, Sebastian

doi:10.1088/1742-6596/2257/1/012004

Cited by 1 publication

(2 citation statements)

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“…Navinchandran [12] also present a systematic workflow for discovering the right KPIs from MWOs to help perform sensitivity-type analyses to determine the significance and influence of identified concepts, which can then be interpreted within the facility context. Lutz et al present a digitalization workflow in the wind energy domain, to extract and structure maintenance data as a basis for reliability KPI calculation [2]. Frank et al [14] present a methodology that uses KPIs and key risk indicators to assess the safety and security of offshore wind farms.…”

Section: Kpi Using Knowledge Discoverymentioning

confidence: 99%

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

Lutz,

Schäfermeier,

Sexton

et al. 2023

Energies

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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 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 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.

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Section: Kpi Using Knowledge Discoverymentioning

confidence: 99%

“…Wind Turbine (WT) MWOs are often unstructured and difficult to use for reliability analysis and decision making [2]. This research explores three separate methods for investigating and extracting reliability Key Performance Indicators (KPIs) from the large volume of MWOs created in the WT industry.…”

Section: Introductionmentioning

confidence: 99%