Fault diagnosis for the gearbox of wind turbine combining ensemble intrinsic time-scale decomposition with Wigner bi-spectrum entropy
Author:
Publisher
JVE International Ltd.
Subject
Mechanical Engineering,General Materials Science
Link
http://www.jvejournals.com/Vibro/fulltextpdf/JVE-17465.pdf
Reference22 articles.
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2. Kusiak A., Li W. The prediction and diagnosis of wind turbine faults. Renewable Energy, Vol. 36, Issue 1, 2011, p. 16-23.
3. Fan A., Li H. A hybrid approach for fault diagnosis of planetary bearings using an internal vibration sensor. Measurement, Vol. 64, 2015, p. 71-80.
4. Kusiak A., Verma A. A data-driven approach for monitoring blade pitch faults in wind turbines. IEEE Transactions on Sustainable Energy, Vol. 2, Issue 1, 2011, p. 87-96.
5. Zhao R. H. Rolling bearing fault diagnosis based on wavelet packet and improved BP neural network for wind turbines. Applied Mechanics and Materials, Vol. 347, 2013, p. 117-120.
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