噪声与振动控制
譟聲與振動控製
조성여진동공제
NOISE AND VIBRATION CONTROL
2014年
3期
182-185
,共4页
振动与波%滚动轴承%独立分量%经验模式分解%AR模型%支持向量机
振動與波%滾動軸承%獨立分量%經驗模式分解%AR模型%支持嚮量機
진동여파%곤동축승%독립분량%경험모식분해%AR모형%지지향량궤
vibration and wave%rolling bearing%ICA%EMD%AR model%SVM
针对滚动轴承非线性的早期故障信号，应用独立分量(ICA)将滚动轴承产生的故障信号从多通道混合信号中分离出来，然后采用EMD (Empirical Mode Decomposition)进行再次降噪并建立AR模型，最后提取模型的自回归参数和残差方差作为故障特征向量，并以此作为支持向量机(SVM)分类器的输入参数来区分滚动轴承的工作状态和故障类型。实验结果表明，该方法是有效的。
針對滾動軸承非線性的早期故障信號，應用獨立分量(ICA)將滾動軸承產生的故障信號從多通道混閤信號中分離齣來，然後採用EMD (Empirical Mode Decomposition)進行再次降譟併建立AR模型，最後提取模型的自迴歸參數和殘差方差作為故障特徵嚮量，併以此作為支持嚮量機(SVM)分類器的輸入參數來區分滾動軸承的工作狀態和故障類型。實驗結果錶明，該方法是有效的。
침대곤동축승비선성적조기고장신호，응용독립분량(ICA)장곤동축승산생적고장신호종다통도혼합신호중분리출래，연후채용EMD (Empirical Mode Decomposition)진행재차강조병건립AR모형，최후제취모형적자회귀삼수화잔차방차작위고장특정향량，병이차작위지지향량궤(SVM)분류기적수입삼수래구분곤동축승적공작상태화고장류형。실험결과표명，해방법시유효적。
Aiming at the early non-linear fault signals of rolling bearings, the ICA is employed to separate the fault signals of the rolling bearing from the mixed signals collected by the multi- channel. Then, the EMD method is used to reduce the noise and establish the AR model. Finally, the self-regressive parameters and the residual square difference of the model are extracted and regarded as the fault characteristic vectors. They are used as the input parameters of the SVM classifier to distinguish the working condition and the type of faults of the rolling bearing. Experimental results show that this approach is effective.
