Artificial Intelligence Tools for Wind Turbine Blade Monitoring
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-66431-1_14
Reference17 articles.
1. Artigao, E., Martin-Martinez, S., Honrubia-Escribano, A., Gomez-Lazaro, E.: Wind turbine reliability: a comprehensive review towards effective condition monitoring development. Appl. Energy 228, 1569–1583 (2018). https://www.sciencedirect.com/science/article/pii/S0306261918310651
2. Badihi, H., Zhang, Y., Jiang, B., Pillay, P., Rakheja, S.: A comprehensive review on signal-based and model-based condition monitoring of wind turbines: fault diagnosis and lifetime prognosis. Proc. IEEE 110(6), 754–806 (2022)
3. Ding, S., Yang, C., Zhang, S.: Acoustic-signal-based damage detection of wind turbine blades: a review. Sensors 23(11) (2023). https://www.mdpi.com/1424-8220/23/11/4987
4. Fu, X., Sheng, M.: Research on structural failure analysis and strengthening design of offshore wind turbine blades. J. Mar. Sci. Eng 10(11) (2022). https://www.mdpi.com/2077-1312/10/11/1661
5. Guo, R.: Strength fitness control system and motor balance based on FPGA and wireless sensors. Microprocess. Microsyst. 81, 103684 (2021). https://www.sciencedirect.com/science/article/pii/S0141933120308309
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