The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism
Abstract
Abstract The complex and changeable working environment of wind turbine often challenges the condition monitoring and fault detection. In this paper, a new method is proposed for fault detection of wind turbine, in which the convolutional neural network (CNN) cascades to the long and short term memory network (LSTM) based on attention mechanism (AM). Supervisory control and data acquisition (SCADA) data are used from wind turbine as input variables and build CNN architecture to extract dynamic changes of data. AM is applied to strengthen the impact of important information. AM can assign different weighs for concentrating the characteristics of LSTM to increase the learning accuracy through mapping weigh and parameter learning. The proposed model can execute early warning for anomaly state and deduce the faulted component by prediction residuals. Finally, through the cases the early failure of the wind turbine is predicted, which verifies the effectiveness of the proposed method.
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Cites
LSTM network: a deep learning approach for short‐term traffic forecast
Zhao, Z., Chen, W., Wu, X., Chen, P. C. Y., & Liu, J. (2017) · IET Intelligent Transport Systems · doi:10.1049/iet-its.2016.0208 · Cited by 1,834
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
Ince, T., Kiranyaz, S., Eren, L., Askar, M., & Gabbouj, M. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2582729 · Cited by 1,407
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
Guo, X., Chen, L., & Shen, C. (2016) · Measurement · doi:10.1016/j.measurement.2016.07.054 · Cited by 782
Jing, L., Zhao, M., Li, P., & Xu, X. (2017) · Measurement · doi:10.1016/j.measurement.2017.07.017 · Cited by 698
Ding, X., & He, Q. (2017) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2674738 · Cited by 489
Deep residual learning-based fault diagnosis method for rotating machinery
Zhang, W., Li, X., & Ding, Q. (2019) · ISA Transactions · doi:10.1016/j.isatra.2018.12.025 · Cited by 416
A review of stochastic resonance in rotating machine fault detection
Lu, S., He, Q., & Wang, J. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.06.032 · Cited by 382
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Zhang, W., Li, X., Jia, X.-D., Ma, H., Luo, Z., & Li, X. (2020) · Measurement · doi:10.1016/j.measurement.2019.107377 · Cited by 364
Wind energy developments and policies in China: A short review
Sahu, B. K. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.183 · Cited by 323
Anomaly detection and fault analysis of wind turbine components based on deep learning network
Zhao, H., Liu, H., Hu, W., & Yan, X. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.05.024 · Cited by 301
Cited by
Badihi, H., Zhang, Y., Jiang, B., Pillay, P., & Rakheja, S. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3171691 · Cited by 319
Su, H., Xiang, L., Hu, A., Xu, Y., & Yang, X. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108765 · Cited by 281
Self-citationAfridi, Y. S., Ahmad, K., & Hassan, L. (2022) · International Journal of Energy Research · doi:10.1002/er.7100 · Cited by 140
Allal, Z., Noura, H. N., Salman, O., & Chahine, K. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120392 · Cited by 130
Metadata
- Authors
- Xiang, L., Wang, P., Yang, X., Hu, A., & Su, H.
- Venue
- Measurement
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 175
- Pages
- 109094
- ISSN
- 0263-2241 (ISSN-L)
- OpenAlex
- W3128775465
- Cited by
- 406
- References
- 30
- FWCI
- 29.24
- Type
- journal article
- Language
- en
- Open access
- closed
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- Xiang, L., Wang, P., Yang, X., Hu, A., & Su, H. (2021). Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism. Measurement. https://doi.org/10.1016/j.measurement.2021.109094
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