Abstract
In the noisy environment, fault characteristics of the composite faults of the wind power gearbox are coupled with each other, which makes the extraction features more difficult. In order to extract the characteristics of composite faults, a new fault diagnosis method for wind power gearbox is proposed in this paper, namely the modified Savitzky Golay Laplacian of Gaussian filter (MSGloG). The method can not only solve the defects that the scale parameters of the Modified Laplacian of Gaussian filter (MloG) filter are not adaptive, but also overcome the problems that the smoothing effect is too much affected by noise. Firstly, determining the Laplace model of Gaussian filter, and using the least square convolution smoothing process to improve the signal-to-noise ratio of the vibration signal. Secondly, a new marginal envelope spectrum entropy index is proposed to measure the complex fault characteristics. Finally, a new chaotic grey wolf optimization algorithm is proposed, which uses the marginal envelope spectral entropy as the fitness function, and the purpose is to make the MSGloG noise reduction adaptive. The method extracted the faults of the bearing outer ring and rolling elements successfully.
Highlights
A Novel Adaptive Fault Diagnosis Method for Wind Power GearboxNENGQUAN DUAN1, JINGTAI WANG 2, TIANSHENG ZHAO3, WENHUA DU1, XIAOMING GUO 4, AND JUNYUAN WANG1
The extraction of fault features in gearboxes in a strong noise environment has always been a difficult point in fault diagnosis research [1]–[4]
EXPERIMENTAL VERIFICATION In order to verify the feasibility of the proposed method in engineering application, the proposed method is applied to the composite fault diagnosis of wind turbine gearbox
Summary
NENGQUAN DUAN1, JINGTAI WANG 2, TIANSHENG ZHAO3, WENHUA DU1, XIAOMING GUO 4, AND JUNYUAN WANG1.
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