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Feature extraction method for bearing composite faults of a wind turbine |
XIANG Ling, LI Ying |
1.Mechanical Engineering Department, North China Electric Power University, Baoding 071003, China |
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Abstract Aiming at the problem that the complex fault characteristics of wind turbine bearings under strong background noise are difficult to accurately extract, a method based on adaptive maximum correlation kurtosis deconvolution (AMCKD) for the wind turbine bearing composite fault feature extraction was proposed.Using the artificial fish swarm algorithm (AFSA), and taking the correlated kurtosis of the deconvoluted signal envelope spectrum as an objective function, the influential parameters of the maximum correlation kurtosis deconvolution algorithm (MCKD) were adaptively optimized.Then, the optimized MCKD was used to deconvolute the original fault signal, and an envelope spectrum analysis was performed on the deconvoluted signal.Through the comparison of the dominant frequency components in the envelope spectrum with the fault characteristic frequency of each component of the bearing, the diagnosis of the bearing composite faults was accurately realized.Simulation and engineering application examples verify the effectiveness and practicability of the proposed method.
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Received: 13 December 2018
Published: 28 April 2020
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