scholarly journals Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis

2020 ◽  
Vol 155 ◽  
pp. 1312-1327 ◽  
Author(s):  
Zhenya Wang ◽  
Ligang Yao ◽  
Yongwu Cai ◽  
Jun Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Support Vector ◽  
Rolling Bearings

Fault Diagnosis of Wind Turbine Gearbox Based on Least Square Support Vector Machine with Genetic Algorithm

2013 ◽  
Vol 846-847 ◽  
pp. 620-623
Author(s):  
Wen Qing Zhao ◽  
Rui Cai ◽  
Li Wei Wang ◽  
De Wen Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Least Square ◽  
Normal Operation ◽  
Support Vector ◽  
Wind Turbine Gearbox ◽  
Vector Method ◽  
Searching Ability

Gearbox affect the normal operation of the wind turbines, to study the fault diagnosis, support vector method was used. Parameters selection is very important and decides the fault diagnosis precision. In order to overcome the blindness of man-made choice of the parameters in least squares support vector machine (LSSVM) and improve the accuracy and efficiency of fault diagnosis, a method based on LSSVM trained by genetic algorithm was proposed. This method searches the optimized parameters in LSSVM by taking advantage of the genetic algorithms powerful global searching ability. The research is provided using this method on the fault diagnosis of wind turbine gearbox and compared with the diagnostic method of LSSVM. The experimental results show that the method achieves a higher diagnostic accuracy.

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