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An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings
Abstract
Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.
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Citations
Cited by
Using SCADA data for wind turbine condition monitoring – a review
Tautz‐Weinert, J., & Watson, S. J. (2017) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2016.0248 · Cited by 458
Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information
Jiang, G., Xie, P., He, H., & Yan, J. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2759301 · Cited by 288
Chen, H., Liu, H., Chu, X., Liu, Q., & Xue, D. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.03.078 · Cited by 253
A global manufacturing big data ecosystem for fault detection in predictive maintenance
Yu, W., Dillon, T., Mostafa, F., Rahayu, W., & Liu, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2915846 · Cited by 225
Saucedo-Dorantes, J. J., Delgado-Prieto, M., Ortega-Redondo, J. A., Osornio-Rios, R. A., & Romero-Troncoso, R. D. J. (2016) · Shock and Vibration · doi:10.1155/2016/5467643 · Cited by 80
Metadata
- Authors
- Bangalore, P., & Tjernberg, L. B.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2015-03
- Volume
- 6
- Issue
- 2
- Pages
- 980-987
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2037411704
- Cited by
- 339
- References
- 0
- FWCI
- 23.51
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bangalore, P., & Tjernberg, L. B. (2015). An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2014.2386305
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