机床与液压
機床與液壓
궤상여액압
MACHINE TOOL & HYDRAULICS
2014年
17期
182-186
,共5页
滚动轴承%经验模态分解%IMF分量%故障诊断%BP神经网络
滾動軸承%經驗模態分解%IMF分量%故障診斷%BP神經網絡
곤동축승%경험모태분해%IMF분량%고장진단%BP신경망락
Rolling bearing%Empirical mode decomposition%Intrinsic mode functions component%Fault diagnosis%BP Neural network
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。
分析瞭滾動軸承故障振動信號的非線性、非平穩性特徵，基于經驗模態分解法（EMD）在處理此類信號中的優勢，研究瞭滾動軸承故障信號的時頻分析處理方法。通過EMD法將滾動軸承故障原始振動信號分解為多箇平穩的IMF分量之和；選取前8箇IMF能量值作為頻域特徵併結閤時域特徵構成故障振動信號特徵集閤，作為BP神經網絡的輸入；建立瞭滾動軸承故障診斷的BP神經網絡模型，利用BP網絡的自學習機製進行網絡訓練，得到瞭輸入特徵與故障模式之間的映射關繫；通過對滾動軸承不同類彆的故障診斷試驗，驗證瞭該方法的可行性。
분석료곤동축승고장진동신호적비선성、비평은성특정，기우경험모태분해법（EMD）재처리차류신호중적우세，연구료곤동축승고장신호적시빈분석처리방법。통과EMD법장곤동축승고장원시진동신호분해위다개평은적IMF분량지화；선취전8개IMF능량치작위빈역특정병결합시역특정구성고장진동신호특정집합，작위BP신경망락적수입；건립료곤동축승고장진단적BP신경망락모형，이용BP망락적자학습궤제진행망락훈련，득도료수입특정여고장모식지간적영사관계；통과대곤동축승불동유별적고장진단시험，험증료해방법적가행성。
Analysis of the rolling bearing fault vibration signal of the nonlinear and non-stationary characteristics,using the meth-od of empirical mode decomposition (EMD)in the treatment of the advantages of this kind of signal,the time-frequency analysis meth-od is studied for the fault signal of the rolling bearing. The rolling bearing vibration signal through the EMD method was decomposed in-to 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.
