摘要
为了提高风电机组传动链的可靠性,提出了一种量子遗传算法优化支持向量机的故障诊断模型。首先确定支持向量机的模型,然后采用量子遗传算法对惩罚参数和核函数系数进行优化。算法使用量子位编码和量子旋转门实现了对初始种群的编码和更新,提高了优化求解的精确度。通过使用优化后的支持向量机模型对传动链的正常工况、表面磨损和齿轮缺齿等3种类型信息的分类诊断,可以有效解决故障诊断的准确率。
In order to improve reliability of wind power unit drivetrain,a fault diagnosis model based on quantum genetic algorithm and support vector machine(SVM)is presented.The model of SVM is conformed,and the penalty parameter and Kernel function coefficient are optimized by quantum genetic algorithm,which coding and renewal of initial population are completed with quantum encoding and rotation gate,the accuracy of optimal solution is improved.Through using the optimized SVM model,with the test and calculation for drivetrain in three types of normal condition,surface wear and missing teeth,the accuracy rate of fault diagnosis can be effectively solved.
作者
刘志刚
赵晓燕
张涛
敖宝林
王俊涛
党齐乾
Liu Zhigang;Zhao Xiaoyan;Zhang Tao;Ao Baolin;Wang Juntao;Dang Qiqian(School of Mechanical Engineering,Henan Polytechnic Institute,Nanyang 473000,China;School of Mechanical Engineering,North China University of Water Conservancy and Hydroelectric Power,Zhengzhou 450046,China;Zhengzhou Research Institute of Mechanical Engineering Co.,Ltd.,Zhengzhou 450052,China;Anyang Iron&Steel Co.,Ltd.,Anyang 455000,China)
出处
《机械传动》
CSCD
北大核心
2018年第9期164-167,共4页
Journal of Mechanical Transmission
基金
河南省教育厅科学技术研究重点项目(14B413006)
河南省高等学校重点科研项目(16A460009)
关键词
风电机组
传动链
故障诊断
支持向量机
量子遗传算法
Wind power unit
Drivetrain
Fault diagnosis
Support vector machine
Quantum genetic algorithm
