The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units
Abstract
Aimed at identifying the health state of wind turbines accurately by comprehensively using the change information in spatial and temporal scale of the supervisory control and data acquisition (SCADA) data, a novel condition monitoring method of wind turbines based on spatio-temporal features fusion of SCADA data by convolutional neural networks (CNN) and gated recurrent unit (GRU) was proposed in this paper. First, missing value complement and selection of variables with Pearson prod-moment correlation coefficient were applied to improve the effectiveness of SCADA data. Second, a deep learning model was constructed by the structures of CNN and GRU. The spatial features in SCADA data were extracted by CNN at every step, and the temporal features in the sequence of spatial features were extracted and fused by GRU. Third, the historical healthy SCADA data was used to train the normal behavior model. At last, the trained model received measured data and output the predicted values. The entire residual between the actual data and the predicted output was calculated to put into the exponential weighted moving average control chart for recognizing the condition of the wind turbine. The effectiveness and availability of the proposed method were proved in measured SCADA data experiments.
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Cites
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317
Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements
Lucas, J. M., & Saccucci, M. S. (1990) · Technometrics · doi:10.1080/00401706.1990.10484583 · Cited by 1,934
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
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
Zhao, R., Wang, D., Yan, R., Mao, K., Shen, F., & Wang, J. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2733438 · Cited by 877
Francis Galton's Account of the Invention of Correlation
Stigler, S. M. (1989) · Statistical Science · doi:10.1214/ss/1177012580 · Cited by 528
Fault diagnosis of wind turbine based on Long Short-term memory networks
Lei, J., Liu, C., & Jiang, D. (2019) · Renewable Energy · doi:10.1016/j.renene.2018.10.031 · Cited by 507
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Xiang, L., Wang, P., Yang, X., Hu, A., & Su, H. (2021) · Measurement · doi:10.1016/j.measurement.2021.109094 · Cited by 406
Badihi, H., Zhang, Y., Jiang, B., Pillay, P., & Rakheja, S. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3171691 · Cited by 319
Deng, Y., Ju, H., Zhai, W., Li, A., & Ding, Y. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3113 · Cited by 55
Metadata
- Authors
- Kong, Z., Tang, B., Deng, L., Liu, W., & Han, Y.
- Venue
- Renewable Energy
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 146
- Pages
- 760-768
- ISSN
- 0960-1481 (ISSN-L)
- OpenAlex
- W2953885557
- Cited by
- 258
- References
- 25
- FWCI
- 13.18
- Type
- journal article
- Language
- en
- Open access
- closed
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