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Impact of using text classifiers for standardising maintenance data of wind turbines on reliability calculations.

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Title: Impact of using text classifiers for standardising maintenance data of wind turbines on reliability calculations.
Authors: Walgern, Julia1,2 (AUTHOR) julia.walgern@iwes.fraunhofer.de, Beckh, Katharina3 (AUTHOR), Hannes, Neele1,4 (AUTHOR), Horn, Martin1,5 (AUTHOR), Lutz, Marc‐Alexander6 (AUTHOR), Fischer, Katharina1 (AUTHOR), Kolios, Athanasios2,7 (AUTHOR)
Source: IET Renewable Power Generation (Wiley-Blackwell). 11/16/2024, Vol. 18 Issue 15, p3463-3479. 17p.
Subject Terms: *Wind power plants, *Wind turbines, Statistical power analysis, Key performance indicators (Management), Acquisition of data
Abstract: This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs. [ABSTRACT FROM AUTHOR]
Copyright of IET Renewable Power Generation (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Impact of using text classifiers for standardising maintenance data of wind turbines on reliability calculations.
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  Data: <searchLink fieldCode="AR" term="%22Walgern%2C+Julia%22">Walgern, Julia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> julia.walgern@iwes.fraunhofer.de</i><br /><searchLink fieldCode="AR" term="%22Beckh%2C+Katharina%22">Beckh, Katharina</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hannes%2C+Neele%22">Hannes, Neele</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Horn%2C+Martin%22">Horn, Martin</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lutz%2C+Marc‐Alexander%22">Lutz, Marc‐Alexander</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fischer%2C+Katharina%22">Fischer, Katharina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kolios%2C+Athanasios%22">Kolios, Athanasios</searchLink><relatesTo>2,7</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22IET+Renewable+Power+Generation+%28Wiley-Blackwell%29%22">IET Renewable Power Generation (Wiley-Blackwell)</searchLink>. 11/16/2024, Vol. 18 Issue 15, p3463-3479. 17p.
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  Data: *<searchLink fieldCode="DE" term="%22Wind+power+plants%22">Wind power plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Wind+turbines%22">Wind turbines</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+power+analysis%22">Statistical power analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Key+performance+indicators+%28Management%29%22">Key performance indicators (Management)</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink>
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  Data: This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IET Renewable Power Generation (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 11/16/2024
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