摘要
为了提高风机地脚螺栓螺母缺失检测精度,提出了一种风机地脚螺栓螺母缺失无损检测方法。利用混沌映射初始化、非线性递减收敛因子和动态权重机制对灰狼算法进行改进,得到寻优精度更高的改进灰狼(Improved Grey Wolf Optimization,IGWO)算法。以螺杆直径、保护层厚度、垫板厚度、磁感应强度为支持向量,螺栓螺母缺失情况为输出量,采用IGWO算法优化SVM参数,构建了基于IGWO-SVM的风机地脚螺栓螺母缺失无损检测模型,算例仿真结果表明,IGWO-SVM模型的检测精度高达95%,检测效果优于其他对比模型,验证了所提方法的有效性。
In order to improve the accuracy of detecting missing anchor bolts and nuts in wind turbines,this paper proposes a non-destructive testing method for missing anchor bolts and nuts in wind turbines.By utilizing chaotic mapping initialization,nonlinear decreasing convergence factor,and dynamic weight mechanism,the improved grey wolf optimizer(IGWO)with higher optimization accuracy is proposed.Using screw diameter,protective layer thickness,pad thickness,and magnetic induction intensity as support vectors and bolt and nut loss as output variables,the IGWO algorithm was used to optimize SVM parameters and construct a non-destructive testing model for wind turbine foundation bolt and nut loss based on IGWO-SVM.The simulation results of the example show that the detection accuracy of the IGWO-SVM model is as high as 95%,and the detection effect is better than other models,verifying the effectiveness of the proposed method.
出处
《现代机械》
2024年第6期73-77,共5页
Modern Machinery
关键词
风电机
螺母缺失
无损检测
改进灰狼算法
支持向量机
wind turbine
nut missing
non-destructive testing
improved grey wolf optimizer
support vector machine
