摘要
针对复合插值包络经验模态分解(CIEEMD)方法存在非平稳系数阈值无法自适应确定的问题,提出了一种改进复合插值包络经验模态分解(ICIEEMD)方法。首先,以边长为ε的网格覆盖振动信号求出其分形盒维数,实现信号非平稳阈值自适应选取,分解得到若干固有模态函数(IMF);其次,结合互相关系数、时域峭度和包络谱峭度建立互相关系数-TE峭度(C-indexTE)复合指标,筛选出有效IMF分量并重构信号,使用Teager能量算子解调获得重构信号的能量谱,实现滚动轴承故障特征提取;最后,基于仿真信号和实验台滚动轴承数据集进行实验分析,与CIEEMD方法和谱峭度法相比,所提方法能够提取出更加清晰的故障特征频率,证明了所提方法的可行性和有效性。
Aiming at the problem that the composite interpolation envelope empirical mode decomposition(CIEEMD)method is lack of self-adaptability in the selection of non-stationary coefficient threshold,an improved composite interpolation envelope empirical mode decomposition(ICIEEMD)method is proposed.Firstly,the fractal box dimension is calculated from the vibration signal covered by grids with side length ofε,and the non-stationary threshold is adaptively selected.After decomposition,some intrinsic mode functions(IMF)are obtained.Secondly,combining with the correlation coefficient,the kurtosises of time domain signal and of envelope spectrum to establish the composite index of correlation coefficient and TE kurtosises(C-indexTE),then the effective IMF components were selected and reconstructed into a new signal.The energy spectrum of the reconstructed signal is obtained by using Teager energy operator,and the fault feature extraction of rolling bearing is realized.Finally,based on the simulation signal and the experimental data set of rolling bearing,the experimental analysis is carried out.The proposed method can extract more clear fault feature frequencies than the CIEEMD and spectral kurtosis methods,which proves the effectiveness and feasibility of the proposed method.
作者
蔡昕一
马军
李祥
Cai Xinyi;Ma Jun;Li Xiang(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Key Laboratory of Artificial Intelligence,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第1期191-203,共13页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(62163020)
云南省基础研究计划项目(202102AD080007)资助
作者简介
蔡昕一,现为昆明理工大学本科生,主要研究方向为机械设备故障诊断。E-mail:cxy_2132@163.com;通信作者:马军,2016年于昆明理工大学获得工学博士学位,现为昆明理工大学硕士生导师,副教授,主要研究方向为机械设备健康管理。E-mail:mjun@kmust.edu.cn
