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基于EEMD形態譜和支持向量機復合的滾動軸承故障診斷方法

Fault diagnosis of ball bearing based on EEMD morphological spectrum and support vector machine

  • 摘要: 針對滾動軸承的內圈、外圈和滾動體故障提出了一種新的診斷方法,該方法融合了集總經驗模態分解(EEMD)、形態譜和支持向量機(SVM)三種方法的優勢.首先,利用經驗模態分解對滾動軸承故障振動信號進行分解,得到若干個具有物理意義的內稟模態分量(IMF);其次,基于最大能量法篩選出含有故障特征信息最豐富的一個內稟模態分量為故障診斷數據源;再次,對數據源在選定尺度范圍內進行形態譜的提取,從而構造故障特征向量;最后,利用支持向量機對滾動軸承的三種故障進行診斷.研究結果表明,該方法能夠有效地診斷出滾動軸承的三種故障,且具有很高的故障診斷正確率.

     

    Abstract: Aiming at fault diagnosis of inner race,outer race and rolling element of ball bearing,a fusion method based on ensemble empirical mode decomposition(EEMD),morphological spectrum,and support vector machine(SVM) was proposed. Firstly,the vibration signal was decomposed by EEMD to get several intrinsic mode functions(IMFs) which have physical meanings. Secondly,the IMF which was rich in fault features was selected as the data source based on power maximum of IMFs. Thirdly,morphological spectrums in some scales of the IMF were extracted,and then they were adopted as the fault eigenvectors. Lastly,the three faults of ball bearing faults were diagnosed by the use of SVM. The conclusion is that the proposed method can diagnosis the faults of the ball bearing with high accuracy.

     

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