计量学报2018，Vol.39Issue(1)：61-65,5.DOI:10.3969/j.issn.1000-1158.2018.01.14
基于分形维数和GA-SVM的风电机组齿轮箱轴承故障诊断
Fault Diagnosis of Wind Turbine Gearbox Bearing Based on Fractal Dimension and GA-SVM
摘要
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.关键词
计量学/轴承故障诊断/风电齿轮箱/分形维数/遗传算法支持向量机/识别准确率Key words
metrology/fault diagnosis of bearing/wind turbine gearbox/fractal dimension/genetic algorithm support vector machine/recognition accuracy分类
通用工业技术引用本文复制引用
时培明,梁凯,赵娜,安淑君..基于分形维数和GA-SVM的风电机组齿轮箱轴承故障诊断[J].计量学报,2018,39(1):61-65,5.基金项目
国家自然科学基金(51475407) （51475407）
