Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal
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DOI: 10.1016/j.renene.2019.11.012
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Cited by:
- Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2023. "A multi-learner neural network approach to wind turbine fault diagnosis with imbalanced data," Renewable Energy, Elsevier, vol. 208(C), pages 420-430.
- Wang, Jingjing & Miao, Yonghao, 2021. "Optimal preventive maintenance policy of the balanced system under the semi-Markov model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
- Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
- Kong, Yun & Han, Qinkai & Chu, Fulei & Qin, Yechen & Dong, Mingming, 2023. "Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox," Renewable Energy, Elsevier, vol. 219(P1).
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Keywords
Wind turbines; Rotary encoder; Adaptive filtering; Gearbox fault diagnosis; Deconvolution;All these keywords.
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