Details
| Original language | English |
|---|---|
| Pages (from-to) | 3463-3479 |
| Number of pages | 17 |
| Journal | IET renewable power generation |
| Volume | 18 |
| Issue number | 15 |
| Publication status | Published - 18 Nov 2024 |
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.
Keywords
- data acquisition, data analysis, reliability, sensitivity analysis, statistical analysis, wind power plants, wind turbines
ASJC Scopus subject areas
Sustainable Development Goals
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In: IET renewable power generation, Vol. 18, No. 15, 18.11.2024, p. 3463-3479.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Impact of using text classifiers for standardising maintenance data of wind turbines on reliability calculations
AU - Walgern, Julia
AU - Beckh, Katharina
AU - Hannes, Neele
AU - Horn, Martin
AU - Lutz, Marc Alexander
AU - Fischer, Katharina
AU - Kolios, Athanasios
N1 - Publisher Copyright: © 2024 The Author(s). IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
PY - 2024/11/18
Y1 - 2024/11/18
N2 - 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.
AB - 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.
KW - data acquisition
KW - data analysis
KW - reliability
KW - sensitivity analysis
KW - statistical analysis
KW - wind power plants
KW - wind turbines
UR - http://www.scopus.com/inward/record.url?scp=85208241329&partnerID=8YFLogxK
U2 - 10.1049/rpg2.13151
DO - 10.1049/rpg2.13151
M3 - Article
AN - SCOPUS:85208241329
VL - 18
SP - 3463
EP - 3479
JO - IET renewable power generation
JF - IET renewable power generation
SN - 1752-1416
IS - 15
ER -