机舱式激光雷达测风仪传动齿轮机械故障诊断研究
Research on Mechanical Fault Diagnosis of the Transmission Gear of the Cabin Lidar Wind Meter
马骁 1韦存海 1李跃朋 1赵亮 1焦波1
作者信息
- 1. 国家电投集团河北电力有限公司,河北 石家庄 050031
- 折叠
摘要
提出了机舱式激光雷达测风仪传动齿轮机械故障诊断方法.利用最小熵反褶积(MED)对齿轮的振动信号去噪处理,利用集成经验模态分解(EEMD)得到齿轮信号的内涵模态(IMF)分量,并根据IMF能量和齿轮峭度建立齿轮故障特征向量,将特征向量输入到最小二乘支持向量机(least squares support vector machine,LSSVM)中,完成传动齿轮机械故障的诊断.实验结果表明,该方法的齿轮故障诊断时间短,根据迭代次数的增加,误差率可控制在3％以下.
Abstract
A method of mechanical fault diagnosis of transmission gear of engine-room LiDAR wind detector is presented.Using minimum entropy deconvolution(MED)to denoise the vibration signal of the gear,the intrinsic mode functions(IMF)components of gear signals are obtained by ensemble empirical mode decomposition(EEMD).According to IMF energy and gear Kurtosis,the gear fault feature vector was established,and the feature vector was input into the least squares support vector machine(LSSVM)to complete the fault diagnosis of the transmission gear machinery.The experiment results shows that the gear fault diagnosis time of this method is short and the error rate remains below 3％with increasing itera-tions.
关键词
齿轮故障诊断/最小熵反褶积/本征模式分量能量/峭度/最小二乘支持向量机Key words
gear fault diagnosis/minimum entropy deconvolution/intrinsic mode component energy/kurtosis/least squares support vector machine引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据