摘要
高速运转会导致轴承产生大量的振动和噪声,使得故障信号与背景噪声相混合,难以准确检测,一旦出现轴承变速故障将会威胁风力发电机的正常运行。为解决这一问题,提出风力发电机高速旋转齿轮箱轴承变速故障信号检测方法。通过小波阈值和经验模态分解方法,对采集到的风电机高速旋转齿轮箱轴承振动信号去噪处理;基于奇异值分解方法,提取风电机高速旋转齿轮箱轴承振动信号特征向量;通过灰狼算法-支持向量机模型,实现对其轴承变速故障检测。实验结果表明,所提方法的风力发电机高速旋转齿轮箱轴承变速故障检测的准确率高。
High speed operation can cause a large amount of vibration and noise in the bearings,making the fault signal mixed with background noise,making it difficult to accurately detect.Once a bearing speed change fault occurs,it will threaten the normal operation of wind turbines.In order to solve this problem,the detection method of variable speed faults in the bearings of the high-speed rotating gearbox of wind turbines is proposed.By using wavelet threshold and empirical mode decomposition methods,the collected vibration signals of high-speed rotating gearbox bearings of wind turbines are denoised;based on Singular value decomposition method,the eigenvector of bearing vibration signal of high-speed rotating gearbox of wind turbine is extracted;by using the grey wolf algorithm support vector machine model,the bearing variable speed fault detection is achieved.The experimental results show that the proposed method has a high accuracy in detecting variable speed faults of bearings in high-speed rotating gearbox of wind turbines.
作者
王韦智
WANG Weizhi(Datang Shanxi New Energy Company,Taiyuan 030000,China)
出处
《机械与电子》
2024年第7期76-80,共5页
Machinery & Electronics
关键词
小波阈值
支持向量机
灰狼算法
变速故障
风力发电机
wavelet threshold
support vector machine
grey wolf algorithm
variable speed fault
wind turbines
