摘要
当轴承出现局部故障,能够表征滚动轴承早期故障的微弱冲击特征在传感器采集的过程中往往被强背景噪声所淹没,且易受信号传输路径的影响,从而导致轴承故障难以诊断。针对上述问题,本文提出一种改进VMD与MOMEDA(Multi-point optimal minimum entropy deconvolution,多点最优最小熵解卷积)的自适应滚动轴承联合降噪方法。首先为了避免VMD方法重要参数严重依赖人工先验知识等问题,采用PSO寻优算法对VMD重要参数进行优化处理,并以峭度作为优化指标选择最优IMF分量,进一步采用MOMEDA消除信号中传输路径的影响,最后结合1.5维能谱诊断滚动轴承故障。与MED-VMD及常规包络谱方法相比较,证明了本文所提方法在轴承故障特征提取领域的优势所在。
When a local fault occurs in the bearing,the weak impact characteristic that can represent the early fault of the rolling bearing is often submerged by strong background noise in the process of sensor acquisition,and it is easily affected by the signal transmission path,thus making it difficult to diagnose the bearing fault.To solve the above problems,an adaptive combined rolling bearing denoising method based on improved variational mode decomposition(VMD)and multi-point optimal minimum entropy deconvolution(MOMEDA)is proposed in this paper.Firstly,in order to solve the problem of VMD which relies heavily on artificial prior knowledge,particle swarm optimization(PSO)is adopted to optimize VMD,and kurtosis is taken as the optimization index to select the optimal IMF component,and then MOMEDA is further adopted to eliminate the influence of the transmission path on the signal.Finally,the rolling bearing fault is diagnosed combined with 1.5D energy spectrum.Compared with MED-VMD and conventional envelope spectrum methods,the advantages of the proposed method are proved in the field of bearing fault feature extraction.
作者
罗世民
黄捷洲
蔡秉桓
LUO Shimin;HUANG Jiezhou;CAI Binghuan(School of Mechatronics&Vehicle Engineering,East China Jiaotong University,Nanchang 330013,China)
出处
《机械科学与技术》
CSCD
北大核心
2022年第3期329-336,共8页
Mechanical Science and Technology for Aerospace Engineering
基金
江西省教育厅科学技术研究项目(GJJ190322)
江西省科技厅工业支撑项目(20151BBE50096)
关键词
峭度
粒子群优化
变分模态分解
1.5维能量谱
kurtosis
particle swarm optimization
variational mode decomposition
1.5D energy spectrum
