本文選題：CEEMD + 廣義形態(tài)差值濾波器　； 參考：《華中師范大學學報(自然科學版)》2017年03期

【摘要】：為了提取滾動軸承早期微弱故障特征信息,提出一種互補總體平均經(jīng)驗?zāi)B(tài)分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)與廣義形態(tài)差值濾波結(jié)合的故障診斷方法.該方法首先對振動信號進行CEEMD分解成若干不同尺度的本征模函數(shù)(Intrinsic Mode Function,IMF)分量,利用相關(guān)系數(shù)-峭度準則來選取故障信息豐富的IMF分量信號,并對其進行重構(gòu);然后采用廣義形態(tài)差值濾波器對重構(gòu)后的信號進行濾波,以濾除噪聲干擾;最后利用Teager能量算子(Teager-Kaiser Energy Operator,TKEO)對去噪后的振動信號進行分析,提取振動信號的故障特征.滾動軸承振動信號分析試驗結(jié)果證明了本文方法的有效性.
[Abstract]:In order to extract the weak fault feature information of rolling bearings in the early stage, a fault diagnosis method based on complementary Ensemble Empirical Mode error filtering and generalized morphological difference filtering is proposed.In this method, the vibration signal is firstly decomposed into intrinsic Mode function (IMF) components of different scales by CEEMD. The correlation-kurtosis criterion is used to select and reconstruct the IMF component signal with abundant fault information.Then the reconstructed signal is filtered by the generalized morphological difference filter to filter the noise interference. Finally, the vibration signal after denoising is analyzed by using the Teager energy operator Teager-Kaiser Energy operator TKEO, and the fault characteristics of the vibration signal are extracted.The result of vibration signal analysis of rolling bearing proves the effectiveness of this method.
【作者單位】： 昆明理工大學信息工程與自動化學院;云南省礦物管道輸送工程技術(shù)研究中心;
【基金】：國家自然科學基金項目(61563024,51169007,61663017) 云南省科技計劃項目(2015ZC005)
【分類號】：TH133.33
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本文編號：1767601