基于MCKD-FDM方法的汽车轴承振动信号降噪  

Car Bearing Vibration Signal Noise Reduction Based on MCKD-FDM Method

作　　者：田萌[1] Tian Meng(Department of Mechanical and Electrical Engineering,Henan Vocational College of Industry and Trade,Zhengzhou Henan 451191,China)

机构地区：[1]河南工业贸易职业学院机电工程系,河南郑州451191

出　　处：《山西电子技术》2024年第3期35-36,74,共3页Shanxi Electronic Technology

基　　金：河南省高等学校重点科研项目(22A470005)。

摘　　要：为了提高电机轴承的故障诊断精度,选择傅里叶分解(FDM)方法把降噪处理信号分解,利用最大相关峭度反褶积(MCKD)重构信号包络谱图实现信息故障的诊断,并开展仿真与实验测试分析。研究结果表明:测试信号形成了明显的故障特征频率与各阶倍频,各阶倍频都发生了幅值降低。采用本文方法可以显著突出故障冲击成分,也可以提取获得丰富轴承故障信息,更明显体现故障特征频率与倍频。本研究故障诊断方法能够满足高精度的汽车传动系统故障检测要求。In order to improve the fault diagnosis accuracy of motor bearing,Fourier decomposition(FDM)method is selected to decompose the noise reduction signal,and the maximum correlation kurtosis deconvolution(MCKD)is used to reconstruct the signal envelope spectrum to diagnose the information fault,and simulation and experimental analysis are carried out.The results show that the test signals form obvious fault characteristic frequency and frequency doubling of each order,and the amplitude of each order frequency doubling is reduced.By using the method presented in this paper,the fault impact components can be significantly highlighted,and rich bearing fault information can be extracted,which more clearly reflects the fault characteristic frequency and frequency doubling.The fault diagnosis method in this paper can meet the requirements of high-precision automotive transmission system fault detection.

关 键 词：轴承 傅里叶分解方法 最大相关峭度反褶积 故障诊断 

分 类 号：TH137[机械工程—机械制造及自动化] TP206.3[自动化与计算机技术—检测技术与自动化装置]