ELMD和MCKD在滚动轴承早期故障诊断中的应用

发布时间：2018-03-28 06:44

本文选题：滚动轴承　切入点：总体局部均值分解　出处：《机械科学与技术》2017年11期

【摘要】：针对滚动轴承早期故障特征信号微弱且受环境噪声影响严重,故障特征信息难以识别的问题,提出了基于总体局部均值分解(Ensemble local mean decomposition,ELMD)和最大相关峭度反褶积(Maximum correlated kurtosis deconvolution,MCKD)的早期故障诊断方法。该方法首先运用ELMD对采集到的振动信号进行分解,得到有限个乘积函数(Product function,PF),由于噪声的干扰,从PF分量的频谱中很难对故障做出正确的判断。然后对包含故障特征的PF分量进行最大相关峭度反褶积处理以消除噪声影响,凸现故障特征信息。最后对降噪信号进行Hilbert包络谱分析,即可从中准确地识别出轴承的故障特征频率。通过轴承故障模拟实验和工程应用实例验证了该方法的有效性与优越性。
[Abstract]:Aiming at the problem that the early fault characteristic signal of rolling bearing is weak and seriously affected by environmental noise, the fault feature information is difficult to identify. An early fault diagnosis method based on Ensemble local mean decompostion (ELMD) and maximum correlated kurtosis deconvolution (MCKD) is proposed. Firstly, the collected vibration signals are decomposed by ELMD. A finite number of product functions are obtained. Due to the noise interference, it is difficult to make a correct judgment on the fault from the spectrum of PF components. Then, the maximum correlation kurtosis deconvolution of PF components with fault features is carried out to eliminate the noise effect. Finally, the fault characteristic frequency of bearing can be accurately identified by the Hilbert envelope spectrum analysis of the noise reduction signal. The effectiveness and superiority of the method are verified by the bearing fault simulation experiment and engineering application.
【作者单位】：内蒙古科技大学机械工程学院;山东交通职业学院泰山校区机电系;
【基金】：国家自然科学基金项目(21366017) 内蒙古高等学校科学研究项目(NYZY16154) 内蒙古科技大学创新基金项目(2015QDL11)资助
【分类号】：TH133.33

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