The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Fault diagnosis of wind turbine based on Long Short-term memory networks
Abstract
Time-series data is widely adopted in condition monitoring and fault diagnosis of wind turbines as well as other energy systems, where long-term dependency is essential to form the classifiable features. To address the issues that the traditional approaches either rely on expert knowledge and handcrafted features or do not fully model long-term dependencies hidden in time-domain signals, this work presents a novel fault diagnosis framework based on an end-to-end Long Short-term Memory (LSTM) model, to learn features directly from multivariate time-series data and capture long-term dependencies through recurrent behaviour and gates mechanism of LSTM. Experimental results on two wind turbine datasets show that our method is able to do fault classification effectively from raw time-series signals collected by single or multiple sensors and outperforms state-of-art approaches. Furthermore, the robustness of the proposed framework is validated through the experiments on small dataset with limited data.
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Attention-based LSTM for Aspect-level Sentiment Classification
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Cited by
Jalayer, M., Orsenigo, C., & Vercellis, C. (2021) · Computers in Industry · doi:10.1016/j.compind.2020.103378 · Cited by 382
Kong, Z., Tang, B., Deng, L., Liu, W., & Han, Y. (2020) · Renewable Energy · doi:10.1016/j.renene.2019.07.033 · Cited by 258
Chen, H., Liu, H., Chu, X., Liu, Q., & Xue, D. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.03.078 · Cited by 253
Application of recurrent neural network to mechanical fault diagnosis: A review
Zhu, J., Jiang, Q., Shen, Y., Qian, C., Xu, F., & Zhu, Q. (2022) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-022-0102-1 · Cited by 200
Afridi, Y. S., Ahmad, K., & Hassan, L. (2022) · International Journal of Energy Research · doi:10.1002/er.7100 · Cited by 140
Metadata
- Authors
- Lei, J., Liu, C., & Jiang, D.
- Venue
- Renewable Energy
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 133
- Pages
- 422-432
- ISSN
- 0960-1481 (ISSN-L)
- OpenAlex
- W2896784509
- Cited by
- 507
- References
- 49
- FWCI
- 27.30
- Type
- journal article
- Language
- en
- Open access
- closed
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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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