摘要
针对最大相关峭度解卷积(Maximum Correlated Kurtosis Deconvolution,MCKD)方法无法自动识别轴承故障脉冲周期,需要对样本进行重采样以及输入参数过多的不足,提出一种自适应MCKD(Adaptive Maximum Correlated Kurtosis Deconvolution,AMCKD)方法。采用包络信号的周期调制强度(Periodic Modulation Intensity,PMI)自适应地确定轴承故障脉冲周期,在迭代求解最优解卷积滤波器的过程中不断更新,使之逐渐逼近真正的故障周期,以实现自适应提取周期性脉冲特征的目标;最后选择相关峭度最大的滤波信号作为最优解卷积信号。与MCKD方法相比,AMCKD方法能够适应性地识别故障脉冲周期,避免了对信号进行重采样,减少了算法的输入参数。仿真和实验结果验证了AMCKD方法在滚动轴承故障特征提取方面的有效性,且与Fast Kurtogram方法的对比结果显示了AMCKD方法在增强周期性脉冲特征方面的优越性。
Considering the shortcomings of the maximum correlated kurtosis deconvolution(MCKD) method that cannot automatically identify the period of bearing fault impulses,exists the resampling process and the multiple input parameters,an adaptive maximum correlated kurtosis deconvolution(AMCKD) method is proposed.The periodic modulation intensity(PMI) of envelope signal is used to identify the period of bearing fault impulses adaptively.Moreover,the period is constantly updated during searching for the optimal deconvolution filter iteratively,so that the real fault period is gradually approximated.Finally,the filtered signal with the largest correlated kurtosis is selected as the optimal deconvolution signal.Compared with MCKD method,AMCKD method can identify fault impulse period adaptively,avoid signal resampling process,and reduce the input parameters of the algorithm.Simulated and experimental results verify the effectiveness of this method in early fault feature extraction of rolling bearings,and the comparison with fast kurtogram method shows the superiority of AMCKD method in enhancing periodic impulse characteristics.
作者
陈丙炎
宋冬利
张卫华
程尧
李佳元
CHEN BingYan;ONG DongLi;ZHANG WeiHua;CHENG Yao;LI JiaYuan(State Key Laboratory of Traction Power,Southwest Jiaotong Uninersity,Chengdu 610031,China)
出处
《机械强度》
CAS
CSCD
北大核心
2020年第6期1293-1301,共9页
Journal of Mechanical Strength
基金
四川省重点研发项目(2019YFG0295)
西南交通大学牵引动力国家重点实验室自主课题(2018TPL-T01)资助。
关键词
特征提取
早期故障
滚动轴承
自适应最大相关峭度解卷积
周期调制强度
Feature extraction
Incipient fault
Rolling element bearings
Adaptive maximum correlated kurtosis deconvolution
Periodic modulation intensity
