| Description |
1 online resource (19 pages) : color illustrations. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
NREL/PR ; 2C00-79167 |
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NREL/PR ; 2C00-79167.
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| Note |
"Drivetrain Reliability Collaborative Workshop."' |
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"February 16-17, 2021." |
| Bibliography |
Includes bibliographical references (page 19). |
| Funding |
National Renewable Energy Laboratory DE-AC36-08GO28308 |
| Note |
Description based on online resource; title from PDF title page (NREL, viewed on Oct. 13, 2021). |
| Summary |
This presentation outlines the work in the paper titled "Prognostics of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data" published by the PHM Society and presented at its 2020 annual conference. It is accessible at https://papers.phmsociety.org/index.php/phmconf/article/download/1292/862. The technical work is on machine learning approaches for prognostics for gearbox faults. The methodology combines SCADA time series data and physics domain modeling data, derived from the models developed by the NREL team, as inputs to machine learning models to predict gearbox bearing failures with one month lead time. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score, and AUC (area under receiver operating characteristic curve). Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision, and F1 score, and AUC is about 33%, and 12%, and 6% respectively, based on the best modeling case in this study. |
| Subject |
Machine learning.
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Supervisory control systems -- United States.
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Wind turbines -- United States.
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Machine Learning |
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Apprentissage automatique.
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Commande supervisée -- États-Unis.
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Éoliennes -- États-Unis.
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Machine learning
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Supervisory control systems
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Wind turbines
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United States https://id.oclc.org/worldcat/entity/E39PBJtxgQXMWqmjMjjwXRHgrq
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| Indexed Term |
bearing |
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gearbox |
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machine learning |
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prognostics |
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wind turbine |
| Genre/Form |
Congress |
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technical reports.
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proceedings (reports)
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Technical reports
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Conference papers and proceedings
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Technical reports.
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Conference papers and proceedings.
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Rapports techniques.
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Actes de congrès.
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| Added Author |
National Renewable Energy Laboratory (U.S.), issuing body.
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| Added Title |
Machine learning for gearbox fault prediction by using both supervisory control and data acquisition and modeled data |
| Standard No. |
1769826 OSTI ID |
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0000-0002-6413-947X |
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0000-0003-0134-0907 |
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0000-0002-3665-4239 |
| Gpo Item No. |
0430-P-09 (online) |
| Sudoc No. |
E 9.22:NREL/PR-2 C 00-79167 |
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