%0 Journal Article
%T 基于MKurt MOMEDA的齿轮箱复合故障特征提取<br>Composite Fault Feature Extraction of Gear Box Based on MKurt-MOMEDA
%A 王志坚
%A 王俊元
%A 赵志芳
%A 吴文轩
%A 张纪平
%A 寇彦飞
%J 振动.测试与诊断
%D 2017
%R 10.16450/j.cnki.issn.1004-6801.2017.04.030
%X 针对齿轮箱中旋转零部件的故障信号是周期性的冲击信号这一特性，提出了一种基于多点峭度（multipoint kurtosis, 简称MKurt）和多点最优最小熵反褶积（multipoint optimal minimum entropy deconvolution adjusted，简称MOMEDA）的齿轮箱复合故障特征提取方法。利用MKurt可以有效提取齿轮箱中被噪声淹没的冲击性振动信号的周期，实现对振动信号振动源的追踪。根据故障的周期设置合理的周期区间，通过MOMEDA对原信号进行降噪，进一步提取原信号的周期性冲击。通过仿真信号和实测数据的分析和验证，证明了MKurt MOMEDA方法可以准确有效地诊断齿轮箱复合故障故障特征。<br>Aiming at the characteristic that the fault signal of the rotating parts in the gear box is a periodic impact signal, a composite fault feature extraction method of gear box based on multipoint kurtosis(Mkurt) and multipoint optimal minimum entropy deconvolution adjusted(MOMEDA) is proposed, namely MKurt-MOMEDA. The use of MKurt can effectively extract the cycle of impact vibration signal submerged by the noise in gear box to achieve the vibration signal vibration source tracking. Then, according to the period of the fault, a reasonable interval is set, and the original signal is denoised by MOMEDA to further extract the periodic impulse of the original signal.Through the analysis and verification of the simulation signal and the measured data, it is proved that the MKurt-MOMEDA method can accurately and effectively diagnose the compound fault characteristics of the gearbox.
%K 多点峭度
%K 最优最小熵反褶积
%K 复合故障
%K 特征提取&lt
%K br&gt
%K multipoint kurtosis
%K multipoint optimal minimum entropy deconvolution adjusted
%K composite fault
%K feature extraction
%U http://zdcs.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=201704030&flag=1