摘要
提出了基于最小熵解卷积(MED)和广义极大极小凹惩罚(GMCP)稀疏增强信号分解的滚动轴承故障诊断的方法。首先引入均方根误差(RMSE)对GMCP能有效保留周期冲击的优势进行定量分析,然后将MED能突出冲击的优势与GMCP结合起来应用到滚动轴承的故障诊断中,该方法能够有效地将设备振动信号的噪声和干扰频率从中分离出来,较完整地保留周期冲击频率,对滚动轴承的故障诊断非常有效,而且在故障特征频谱的识别上同样具有优势。仿真和实验证明了该方法在滚动轴承故障诊断领域的竞争力。
In this paper,a new fault diagnosis method for rolling bearings based on minimum entropy deconvolution(MED)and generalized minimax concave penalty(GMCP)sparse enhanced signal decomposition is proposed.First,root mean square error(RMSE)is introduced to quantitatively analyze the advantages of GMCP effectively retaining periodic impact.Then MED and GMCP are applied to the fault diagnosis of rolling bearings.This method can effectively separate the noise and interference frequencies of equipment vibration signals and retain the periodic impulse frequencies completely.It is very effective for the fault diagnosis of rolling bearings,and it also has advantages in the identification of fault characteristic spectrum.The competitiveness of this method in fault diagnosis of rolling bearings is verified by simulation and experiment.
作者
肖凌俊
吕勇
袁锐
Xiao Lingjun;LüYong;Yuan Rui(Key Laboratory of Metallurgical Equipment and Control Technology,Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China;Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《机械科学与技术》
CSCD
北大核心
2020年第2期165-173,共9页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51475339,51405353)资助.
关键词
滚动轴承
凹惩罚
最小熵解卷积
特征提取
信号分析
rolling bearing
concave penalty
minimax entropy deconvolution
feature extraction
signal processing
fault diagnosis
vibration signal
