作者：荆双喜；李新华；朱昆鸣；冷军发；罗晨旭

第一作者单位：河南理工大学机械与动力工程学院

摘要：滚动轴承早期故障振动信号微弱,并且受环境噪声影响严重,特征信号提取困难。针对这一问题,提出了最大相关峭度解卷积方法来提取轴承故障的特征信号。通过计算信号的最大相关峭度值,估算出感兴趣的解卷积周期T,选择合适的时延步数M,对故障信号做最大相关峭度解卷积,并对最大相关峭度解卷积滤波后的信号进行包络解调,提取出滚动轴承的故障特征,实现了滚动轴承的早期故障诊断。仿真和实验验证了该方法在滚动轴承故障诊断中的有效性。

Abstract：The faulty vibration signal of rolling element bearing at initial stage is generally very weak and affected by environment noise seriously.So it is difficult to extract the fault feature.In order to solve this problem,the method of maximum correlated kurtosis deconvolution (MCKD)is proposed.Firstly,the interested de-convolutive period T is estimated by calculating the maximum correlated kurtosis value of signals;Then,the appropriate M-shift is selected to do the MCKD for the fault signal;Finally,the fault feature of rolling-bearing is extracted through envelope demodulation analysis method,and is diagnosed as the incipient fault for this rolling-bearing.Its effectiveness is verified by the simulations and tests.

基金：国家自然科学基金资助项目(U1304523)；中国煤炭工业协会指导性项目(MTKJ2015-261)；

关键词：滚动轴承;早期故障;最大相关峭度解卷积;包络解调;

DOI：10.16186/j.cnki.1673-9787.2018.01.012

分类号：TH133.33