现代制造工程Issue(4)：142-148,7.DOI:10.16731/j.cnki.1671-3133.2017.04.027
变分框架下多尺度熵相关优化的模态分解在故障诊断中的应用
Application of multi-scale entropy correlation optimization to mode decomposition in fault diagnosis under variational framework
摘要
Abstract
According to optimal mode selection and key parameter identification of a new adaptive mode decomposition under variational framework Variational Mode Decomposition (VMD),from the idea of binary search,the multi-scale entropy correlation and correlation coefficient in Fourier domain are presented to solve the problem above for VMD,and its filtering essence is revealed through decomposition characteristics of bearing fault simulation signal in Fourier domain.With analysis for simulation signal and engineering application of bearing fault,the results show that,compared with Empirical Mode Decomposition(EMD) and Ensemble Empirical Mode Decomposition(EEMD),de-noising effect of the Improved VMD (IVMD) is more obvious,which is an effective adaptive mode decomposition method in Fourier domain,and can extract the weak feature frequency of fault signal more accurately,achieve correct recognition of bearing fault.关键词
变分/最优模态/参数辨识/故障诊断/多尺度熵相关系数Key words
variational/optimal mode/parameter identification/fault diagnosis/multi-scale entropy correlation coefficient分类
信息技术与安全科学引用本文复制引用
李沁雪,张清华,崔得龙,舒磊,黄剑锋..变分框架下多尺度熵相关优化的模态分解在故障诊断中的应用[J].现代制造工程,2017,(4):142-148,7.基金项目
国家自然科学基金项目(61174113,61672174) （61174113,61672174）
广东省自然科学基金项目(2016A030307029) （2016A030307029）
