摘要
针对强背景噪声下滚动轴承微弱故障特征提取问题,提出了一种基于参数自适应优化变分模态分解(VMD)与多点最优最小熵解卷积(MOMEDA)相结合的轴承故障特征提取方法。首先对滚动轴承时域振动信号进行VMD分解,然后基于自相关函数脉冲谐波噪声比指标(AIHN)最大化原则进行挑选得到最佳模态分量(BIMF)并对其进行MOMEDA滤波,包络解调后得到故障特征频率,最后将本文所提方法体应用于数值仿真信号上可以明显观察到故障特征频率131.1 Hz,应用于实际轴承故障信号可以有效识别轴承故障特征频率294.5 Hz,与原始包络谱提取的311 Hz以及MCKD提取的320 Hz相比更加接近理论故障特征频率294 Hz。
Aiming at the problem that the weak fault features of rolling bearings are difficult to extract,a bearing fault feature extraction method based on the combination of parameter adaptive optimization variable modal decomposition(VMD)and multi-point optimal minimum entropy deconvolution(MOMEDA)is proposed.Firstly,the VMD decomposition is performed on the rolling bearing time domain vibration signal,and then the best mode component(BIMF)is selected based on the principle of maximizing the index of impulse harmonic noise ratio(AIHN)of autocorrelation function and MOMEDA filtering is performed on it,and the fault characteristic frequency is obtained after envelope deconvolution,and finally the fault characteristic frequency can be clearly observed by applying the proposed method body to the numerical simulation signal 131.1 Hz,which can be applied to the actual bearing fault signal to effectively identify the bearing fault characteristic frequency of 294.5 Hz,which is closer to the theoretical fault characteristic frequency 294 Hz compared with 311 Hz extracted by the original envelope spectrum and 320 Hz extracted by MCKD.
作者
阮强
王贵勇
刘韬
王廷轩
Ruan Qiang;Wang Guiyong;Liu Tao;Wang Tingxuan(Faculty of Mechanical and Electrical Engineering,Kunming University of Science and Technology,Kunming 650500,China;Engineering Research Center for Intelligent Maintenance of Advanced Equipment of Yunnan Province,Kunming 650500,China)
出处
《电子测量技术》
北大核心
2022年第1期165-171,共7页
Electronic Measurement Technology
基金
国家自然科学基金(52065030,51875272)
云南省重大科技专项计划(202002AC080001)项目资助。
