基于CEEMDAN和双重峭度准则的电动机轴承故障特征频率估计方法OA
A Fault Characteristic Frequency Estimation Method of Motor Bearing Based on CEEMDAN and Double Kurtosis Criterion
振动传感器采集的轴承故障信号极易被强噪声污染,导致故障特征频率估计精度恶化.针对该问题,提出一种基于自适应噪声完备集合经验模态分解(CEEMDAN)和双峭度准则的轴承故障特征频率高精度估计方法.使用CEEMDAN完成振动信号分解之后,从众多的备选模态中挑选出合适成分重构故障特征信号极具挑战.对信号分解获得的模态分量进行迷向圆变换(标准白化处理)后,噪声对应的模态分量的分布更接近于正态分布.借助该信息,引入双重峭度准则,第一重峭度判定是在原始模态分…查看全部>>
Bearing fault signal collected by vibration sensor is easily polluted by strong noise,which degrades the accuracy of the fault characteristic frequency estimation.For this problem,a high accurate method of the bearing fault frequency estimation based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and double kurtosis criterion is proposed.It is very challenging to select the appropriate components from alternative modes to recon…查看全部>>
解春维;申伟霖;余美仪
广州市机电技师学院,广州 510435佛山科学技术学院,广东佛山 528225佛山科学技术学院,广东佛山 528225
机械工程
自适应噪声完备集合经验模态分解轴承故障检测峭度故障诊断
complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)bearing fault detectionkurtosisfault diagnosis
《机电工程技术》 2024 (2)
75-79,5
国家自然科学基金资助项目(61972092)
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