基于CEEMD和奇異值差分譜的滾動軸承故障特征提取
發(fā)布時間：2018-09-01 18:16
【摘要】：針對滾動軸承故障信號非線性、非平穩(wěn)特征導(dǎo)致的故障頻率難以提取的問題,提出一種基于補(bǔ)充總體平均經(jīng)驗(yàn)?zāi)B(tài)分解(Complementary EEMD,CEEMD)和奇異值差分譜結(jié)合的滾動軸承故障診斷方法。CEEMD分解向原信號成對地添加符號相反的白噪聲,幾乎消除殘留白噪聲的影響。首先,對故障信號利用CEEMD算法進(jìn)行分解,得到若干IMF(Intrinsic Mode Function)分量,然后運(yùn)用相關(guān)系數(shù)—峭度準(zhǔn)則對IMF分量進(jìn)行篩選并重構(gòu),再對重構(gòu)信號進(jìn)行奇異值分解,并求出奇異值差分譜,根據(jù)奇異值差分譜理論進(jìn)行消噪和重構(gòu),最后對重構(gòu)信號進(jìn)行Hilbert包絡(luò)譜分析,提取故障頻率。實(shí)驗(yàn)結(jié)果表明,提出的方法,能精確地提取滾動軸承的故障頻率。
[Abstract]:Aiming at the problem that the fault signal of rolling bearing is nonlinear and the fault frequency caused by non-stationary characteristic is difficult to extract, A rolling bearing fault diagnosis method based on complementary average empirical mode decomposition (Complementary EEMD,CEEMD) and singular value differential spectrum is proposed. The white noise with opposite symbols is added to the original signal in pairs, which almost eliminates the influence of residual white noise. Firstly, the fault signal is decomposed by CEEMD algorithm, and some IMF (Intrinsic Mode Function) components are obtained. Then the correlation coefficient kurtosis criterion is used to screen and reconstruct the IMF component, and then the singular value decomposition of the reconstructed signal is carried out, and the singular value difference spectrum is obtained. According to the singular value difference spectrum theory, the noise reduction and reconstruction are carried out. Finally, the Hilbert envelope spectrum of the reconstructed signal is analyzed, and the fault frequency is extracted. The experimental results show that the proposed method can accurately extract the fault frequency of rolling bearings.
【作者單位】： 華北電力大學(xué)能源動力與機(jī)械工程學(xué)院;
【分類號】：TH133.33
本文編號：2217971
[Abstract]:Aiming at the problem that the fault signal of rolling bearing is nonlinear and the fault frequency caused by non-stationary characteristic is difficult to extract, A rolling bearing fault diagnosis method based on complementary average empirical mode decomposition (Complementary EEMD,CEEMD) and singular value differential spectrum is proposed. The white noise with opposite symbols is added to the original signal in pairs, which almost eliminates the influence of residual white noise. Firstly, the fault signal is decomposed by CEEMD algorithm, and some IMF (Intrinsic Mode Function) components are obtained. Then the correlation coefficient kurtosis criterion is used to screen and reconstruct the IMF component, and then the singular value decomposition of the reconstructed signal is carried out, and the singular value difference spectrum is obtained. According to the singular value difference spectrum theory, the noise reduction and reconstruction are carried out. Finally, the Hilbert envelope spectrum of the reconstructed signal is analyzed, and the fault frequency is extracted. The experimental results show that the proposed method can accurately extract the fault frequency of rolling bearings.
【作者單位】： 華北電力大學(xué)能源動力與機(jī)械工程學(xué)院;
【分類號】：TH133.33
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