Fault Diagnosis Method of Wind Turbine Bearing Based on Improved Intrinsic Time-scale Decomposition and Spectral Kurtosis

被引：0
|
作者：
Zhang, Ying [1 ]
Zhang, Chao [2 ]
Liu, Xinyuan [1 ]
Wang, Wei [1 ]
Han, Yu [1 ]
Wu, Na [1 ]
机构：
[1] State Grid Shanxi Elect Power Res Inst, Taiyuan, Peoples R China
[2] State Grid Shanxi Elect Power Co, Taiyuan, Peoples R China
来源：
2019 ELEVENTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI 2019) | 2019年
关键词：
wind turbine bearing; fault diagnosis; improved intrinsic time-scale decomposition; spectrum kurtosis;
D O I：
10.1109/icaci.2019.8778629
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the linear transformation of intrinsic time-scale Decomposition (ITD) method and cubic spline interpolation, this paper proposes an Improved Intrinsic Time-scale Decomposition method (MD). The IITD method and Spectrum Kurtosis (SK) are combined to realize the intelligent diagnosis of bearing faults. Simulation and experimental results show that the IITD-SK method proposed in this paper successfully extracts the fault feature frequency, and can realize effective diagnosis of bearing faults. Compared with the results of traditional Fourier transform, envelope spectrum analysis and EMD method, this method has a better diagnosis effect.
引用
收藏
页码：29 / 34
页数：6
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