The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
A review of wind turbine bearing condition monitoring: State of the art and challenges
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Citations
Cites
Condition monitoring of wind turbines: Techniques and methods
García Márquez, F. P., Tobias, A. M., Pinar Pérez, J. M., & Papaelias, M. (2012) · Renewable Energy · doi:10.1016/j.renene.2012.03.003 · Cited by 878
Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B., & Fnaiech, F. (2015) · Applied Acoustics · doi:10.1016/j.apacoust.2014.08.016 · Cited by 768
Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression
Soualhi, A., Medjaher, K., & Zerhouni, N. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2014.2330494 · Cited by 665
Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges
Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T., & Ekemb, G. (2014) · Energies · doi:10.3390/en7042595 · Cited by 604
Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2014.10.014 · Cited by 502
Wind turbine condition monitoring: technical and commercial challenges
Yang, W., Tavner, P. J., Crabtree, C. J., Feng, Y., & Qiu, Y. (2014) · Wind Energy · doi:10.1002/we.1508 · Cited by 474
Wind turbine condition monitoring by the approach of SCADA data analysis
Yang, W., Court, R., & Jiang, J. (2013) · Renewable Energy · doi:10.1016/j.renene.2012.11.030 · Cited by 400
Cost-Effective Condition Monitoring for Wind Turbines
Yang, W., Tavner, P. J., Crabtree, C. J., & Wilkinson, M. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2009.2032202 · Cited by 344
Vibration analysis for bearing fault detection and classification using an intelligent filter
Zarei, J., Tajeddini, M. A., & Karimi, H. R. (2014) · Mechatronics · doi:10.1016/j.mechatronics.2014.01.003 · Cited by 269
Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis
Wang, T., Liang, M., Li, J., & Cheng, W. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.11.011 · Cited by 228
Cited by
Yu, J., Ding, B., & He, Y. (2018) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-018-1018-7 · Cited by 28
Guo, J., Lu, S., Zhai, C., & He, Q. (2018) · Measurement Science and Technology · doi:10.1088/1361-6501/aa92d6 · Cited by 25
Metadata
- Authors
- de Azevedo, H. D. M., Araújo, A. M., & Bouchonneau, N.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 56
- Pages
- 368-379
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W2196381116
- Cited by
- 382
- References
- 44
- FWCI
- 18.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- de Azevedo, H. D. M., Araújo, A. M., & Bouchonneau, N. (2016). A review of wind turbine bearing condition monitoring: State of the art and challenges. Renewable and Sustainable Energy Reviews. https://doi.org/10.1016/j.rser.2015.11.032
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
