摘要
分析了滚动轴承故障振动信号的非线性、非平稳性特征,基于经验模态分解法(EMD)在处理此类信号中的优势,研究了滚动轴承故障信号的时频分析处理方法。通过EMD法将滚动轴承故障原始振动信号分解为多个平稳的IMF分量之和;选取前8个IMF能量值作为频域特征并结合时域特征构成故障振动信号特征集合,作为BP神经网络的输入;建立了滚动轴承故障诊断的BP神经网络模型,利用BP网络的自学习机制进行网络训练,得到了输入特征与故障模式之间的映射关系;通过对滚动轴承不同类别的故障诊断试验,验证了该方法的可行性。
Analysis of the rolling bearing fault vibration signal of the nonlinear and non-stationary characteristics,using the method of empirical mode decomposition（EMD） in the treatment of the advantages of this kind of signal,the time-frequency analysis method is studied for the fault signal of the rolling bearing. The rolling bearing vibration signal through the EMD method was decomposed into a number of stable sum of intrinsic mode functions（IMF） component. Selected the top eight IMF energy value as frequency domain features and combined with time domain features,sum of features of fault vibration signals were constructed as the BP neural network input. The BP neural network model of rolling bearing fault diagnosis was set up,by using the BP network self-learning mechanism for network training,the mapping relationship between input features and fault mode was gotten. Based on different categories of rolling bearing fault diagnosis experiment,proves the feasibility of this method is proved.
出处
《机床与液压》
北大核心
2014年第17期182-186,共5页
Machine Tool & Hydraulics
基金
国家自然科学基金项目(51075220)
青岛市科技计划基础研究项目(12-1-4-4-(3)-JCH)
关键词
滚动轴承
经验模态分解
IMF分量
故障诊断
BP神经网络
Rolling bearing
Empirical mode decomposition
Intrinsic mode functions component
Fault diagnosis
BP Neural network
