The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Machine learning methods for wind turbine condition monitoring: A review
Open accessAbstract
This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML steps, including data sources, feature selection and extraction, model selection (classification, regression), validation and decision-making. Our findings show that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression. Neural networks, support vector machines and decision trees are most commonly used. We conclude with a discussion of the main areas for future work in this domain.
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Cites
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Willmott, C., & Matsuura, K. (2005) · Climate Research · doi:10.3354/cr030079 · Cited by 6,512 · cited 1×
“Choosing lower norms k typically results in measures being less sensitive to outliers with some papers arguing for the use of MAE over RMSE [132].”
MethodThe Building Blocks of Interpretability
Olah, C., Satyanarayan, A., Johnson, I., Carter, S., Schubert, L., Ye, K., & Mordvintsev, A. (2018) · Distill · doi:10.23915/distill.00010 · Cited by 591
Cited by
Applications of machine learning to machine fault diagnosis: A review and roadmap
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258
An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems
Gao, Z., & Liu, X. (2021) · Processes · doi:10.3390/pr9020300 · Cited by 281
Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis
Jin, X., Xu, Z., & Qiao, W. (2021) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2020.2989220 · Cited by 209
Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
Maldonado-Correa, J., Martín-Martínez, S., Artigao, E., & Gómez-Lázaro, E. (2020) · Energies · doi:10.3390/en13123132 · Cited by 183
Metadata
- Authors
- Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., & Nenadic, G.
- Venue
- Renewable Energy
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 133
- Pages
- 620-635
- ISSN
- 0960-1481 (ISSN-L)
- OpenAlex
- W2897250207
- Cited by
- 831
- References
- 6
- FWCI
- 41.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., & Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy. https://doi.org/10.1016/j.renene.2018.10.047
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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