Author:
Chen Huan,Hsu Jyh-Yih,Hsieh Jia-You,Hsu Hsin-Yao,Chang Chia-Hao,Lin Yu-Ju
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials
Reference29 articles.
1. R. Adib, H. E. Murdock, F. Appavou, A. Brown, B. Epp, A. Leidreiter and T. C. Farrell, Renewables 2015 Global Status Report, REN21 Secretariat, Paris (2015) 162.
2. L. Wang, Z. Zhang, J. Xu and R. Liu, Wind turbine blade breakage monitoring with deep autoencoders, IEEE Transactions on Smart Grid, 9 (4) (2016) 2824–2833.
3. Y. Cui, P. Bangalore and L. B. Tjernberg, An anomaly detection approach based on machine learning and SCADA data for condition monitoring of wind turbines, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), IEEE (2018) 1–6.
4. P. Kamat and R. Sugandhi, Anomaly detection for predictive maintenance in Industry 4.0-a survey, E3S Web of Conferences, EDP Sciences, 170 (2020) 02007.
5. A. Stetco, F. Dinmohammadi, X. Zhao, V. Robu, D. Flynn, M. Barnes and G. Nenadic, Machine learning methods for wind turbine condition monitoring: a review, Renewable Energy, 133 (2019) 620–635.
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