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| Fault Diagnosis of Wind Turbine Gearbox Bearing Based on Fractal Dimension and GA-SVM |
| SHI Pei-ming,LIANG Kai,ZHAO Na,AN Shu-jun |
| School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract For wind turbine gearbox bearing fault diagnosis is studied, and a fault diagnosis method based on the fractal dimension and genetic algorithm support vector machine (GA-SVM) is put forward. Based on the commonly used time domain feature parameters as the support vector machine identification parameters, the fractal dimension feature parameters are introduced to enhance the recognition accuracy of support vector machines. The model of support vector machine parameters optimization based on genetic algorithm is proposed, and the optimal support vector machine parameters are obtained by the optimization of GA. Using the gear box bearing data from a wind farm in Zhangjiakou, Hebei province for fault diagnosis. Experimental results show that the proposed model GA-SVM provided a good solution to the parameter selection problem, as well as the characteristic parameters based on fractal dimension also improve the recognition accuracy of wind turbine bearing failure.
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Received: 16 April 2016
Published: 29 December 2017
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Corresponding Authors:
pei-ming shi
E-mail: spm@ysu.edu.cn
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2018, Vol. 39 
