%0 Journal Article
%T 基于谱峭度和最大相关峭度解卷积的滚动轴承复合故障特征分离方法
%A 胡爱军
%A 赵军
%A 孙尚飞
%A 黄申申
%J 振动与冲击
%D 2019
%X 针对振动信号中复合故障特征难以准确分离的问题，提出了一种融合谱峭度(spectral kurtosis，简称SK)和最大相关峭度解卷积(Maximum correlated kurtosis deconolution，简称MCKD)的复合故障分离方法。首先对复合故障信号做谱峭度分析，根据选择的各共振频带对信号进行带通滤波，提取出多个故障信号；然后对提取的各信号做包络解调分析，对能提取出单一故障特征的振动信号完成分离过程；对未提取出单一故障特征的振动信号最后做最大相关峭度解卷积处理。采用改进的轴承复合故障仿真模型验证了方法的有效性。实测滚动轴承内、外圈复合故障信号分析结果表明，该方法能够实现复合故障的准确分离。</br>Abstract：Aiming at the problem that the compound fault features in vibration signal is difficult to be separated accurately,a compound fault separation method was proposed based on spectral kurtosis (SK) and maximum correlated kurtosis deconvolution (MCKD).Firstly,the fault signal was analyzed by spectral kurtosis,and resonance band was selected to carry out band-pass filtering to extract several fault signals.Secondly,the envelope demodulation method was used to analyze the extract vibration signals,complete the separation process for the vibration signal that can extract single fault feature.Finally,the maximum correlation kurtosis deconvolution was applied to the vibration signals that can separate single fault feature.The effectiveness of the method was verified by the improved compound fault simulation model.The analysis results of measured fault signals of rolling bearing show that the method can realize accurate separation of compound faults.
%K 滚动轴承
%K 复合故障
%K 特征分离
%K 谱峭度
%K 最大相关峭度解卷积&lt
%K /br&gt
%U http://jvs.sjtu.edu.cn/CN/abstract/abstract8286.shtml