摘要
针对当前机械故障自适应时频分析存在模态混叠和时频分辨率低的问题,提出一种变分模态分解(Variational Mode Decomposition,VMD)和Choi-Williams分布(Choi-Williams Distribution,CWD)相结合的齿轮故障诊断方法。首先参考VMD分解后各分量的中心频率,确定模态分量个数,将采集到的齿轮故障信号分解为指定个数的多个单分量有限带宽固有模态分量(Band-limited Intrinsic Mode Functions,BLIMFS);然后结合峭度(Kurtosis,K)和互相关系数(Cross-correlation,CC)准则,去除虚假分量,筛选出真实的,包含丰富故障特征信息的模态分量;最后将筛选出的多个模态分量进行CWD时频分布表示,结合时频域表现出的频率与等时冲击特性,识别出齿轮故障特征。通过齿轮断齿故障仿真和实验分析,以及与EMD-WVD方法的应用效果对照,验证了文中所论方法的有效性和适用性。
Facing the problems of adaptive time-frequency analysis in mechanical fault diagnosis,such as modal aliasing and low time-frequency resolution,a gear fault diagnosis method,which combines variational mode decomposition(VMD)with Choi-Williams distribution(CWD),is proposed in this paper.Firstly,referring to the center frequency of each component after VMD decomposition,the number of modal components is determined,and the collected gear fault signal is decomposed into a specified number of single-component limited bandwidth inherent modal components(BLIMFS).Then,combining kurtosis(K)and cross-correlation(CC)criteria,spurious components are removed,and true modal components with rich feature information are selected.Finally,the selected modal components are represented in the CWD time-frequency spectrogram,and the characteristics of the gear fault are identified by combining the frequency and isochronal impact features exhibited in the time-frequency domain.The effectiveness and applicability of the method proposed in this paper are verified by the fault simulation and experimental analysis,and fault type is gear tooth fault.The application effect of the EMD-WVD method is also used as a contradistinction.
作者
张永鑫
宋晓庆
张佳琛
张晓冬
ZHANG Yongxin;SONG Xiaoqing;ZHANG Jiachen;ZHANG Xiaodong(School of Information and Electromechanical Engineering,Zhengzhou Business University,Gongyi Henan 451200,China)
出处
《机械设计与研究》
CSCD
北大核心
2020年第3期50-55,共6页
Machine Design And Research
基金
国家自然科学基金资助项目(U1604140,U1304523)
河南省科技攻关项目(172102210021)。
