scholarly journals Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 33583-33588 ◽  
Author(s):  
Xingtong Zhu ◽  
Jianbin Xiong ◽  
Qiong Liang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Support Vector ◽  
Quantum Genetic Algorithm

Fault Diagnosis for Rolling Bearing Based on Genetic-SVM Classifier

2011 ◽  
Vol 199-200 ◽  
pp. 620-624 ◽  
Author(s):  
Yun Jie Xu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnostic Model ◽  
Roller Bearings ◽  
Study Support ◽  
Frequency Demodulation

Fault diagnosis of roller bearings is very complex, so it is difficult to use the mathematical model to describe their faults. Whose developmental changes have dual trends of increase and fluctuation. In this study, support vector machine trained by genetic algorithm based on high frequency demodulation analysis is proposed to fault diagnosis of ball bearing. Genetic algorithm is used to determine training parameters of support vector machine in this model, which can optimize the support vector machine (SVM) an intelligent diagnostic model. The performance of the GSVM system proposed in this study is evaluated by roller bearings in the wood-wool production device. The experimental results indicate that the proposed support vector machine trained by genetic algorithm has good diagnosis results in the application.

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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