Data‐driven multiscale sparse representation for bearing fault diagnosis in wind turbine

Wind Energy ◽  
2019 ◽  
Vol 22 (4) ◽  
pp. 587-604 ◽  
Author(s):  
Yanjie Guo ◽  
Zhibin Zhao ◽  
Ruobin Sun ◽  
Xuefeng Chen
Measurement ◽  
2015 ◽  
Vol 74 ◽  
pp. 70-77 ◽  
Author(s):  
W.Y. Liu ◽  
Q.W. Gao ◽  
G. Ye ◽  
R. Ma ◽  
X.N. Lu ◽  
...  

Author(s):  
Xueli An ◽  
Luoping Pan

For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.


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