Detection for Incipient Damages of Wind Turbine Rolling Bearing Based on VMD-AMCKD Method

Incipient damages of wind turbine rolling bearing are very difficult to be detected because of the interference of multi-frequency components and strong ambient noise. To solve this problem, this paper proposes a new detected method named VMD-AMCKD, combining complementary advantages of variational...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.67944-67959
Main Authors: Zhang, Jun, Zhang, Jianqun, Zhong, Min, Zhong, Jianhua, Zheng, Jinde, Yao, Ligang
Format: Article
Language:English
Subjects:
adaptive maximum correlated kurtosis deconvolution
Algorithms
Damage detection
Data centers
Decomposition
Deconvolution
Devolution
grasshopper optimization algorithm
Incipient damage detection
Information filters
Kurtosis
Mathematical model
Optimization
Random noise
Roller bearings
rolling bearing
Rolling bearings
variational mode decomposition
Wind damage
wind turbine
Wind turbines
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Summary:Incipient damages of wind turbine rolling bearing are very difficult to be detected because of the interference of multi-frequency components and strong ambient noise. To solve this problem, this paper proposes a new detected method named VMD-AMCKD, combining complementary advantages of variational mode decomposition (VMD) and adaptive maximum correlated kurtosis deconvolution (AMCKD). A novel index is proposed to screen out the most sensitive mode containing fault information after VMD decomposition. The mode also can determine a suspectable range for the fault frequency, based on which the optimized range of devolution period T in MCKD can be pre-determined. The Grasshopper optimization algorithm (GOA) is adapted to adaptively select the key parameters in MCKD. The proposed method can successfully diagnose the simulated signal mixed with strong white Gaussian noise. Its robustness is further proven by the diagnosis for three different types of experimental signal from CWRU bearing data center. Finally, the VMD-AMCKD is applied to detect incipient damages of rolling bearings in a laboratory wind turbine.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2918343