發(fā)布時(shí)間：2018-08-04 18:56

【摘要】：針對變分框架下,一種新的模態(tài)分解——變分模態(tài)分解(Variational Mode Decomposition,VMD)的最優(yōu)模態(tài)分量選擇和關(guān)鍵參數(shù)辨識問題,借鑒折半查找的思想,提出應(yīng)用多尺度熵相關(guān)系數(shù)和頻域相關(guān)系數(shù)來改進(jìn)VMD的上述關(guān)鍵環(huán)節(jié),并通過軸承故障信號仿真研究其頻域分解的數(shù)據(jù)特點(diǎn),揭示其濾波本質(zhì);軸承故障信號仿真及工程應(yīng)用的結(jié)果表明,相對于經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition,EMD)和總體平均經(jīng)驗(yàn)?zāi)B(tài)分解(Ensemble Empirical Mode Decomposition,EEMD),改進(jìn)后的VMD(IVMD)去噪效果更為明顯,是一種有效的自適應(yīng)頻域模態(tài)分解方法,可更為準(zhǔn)確地提取出微弱特征頻率信息,實(shí)現(xiàn)軸承故障的正確識別。
[Abstract]:In view of the problem of optimal modal component selection and key parameter identification for a new mode decomposition-variational mode decomposition (Variational Mode DecompositionVMD) framework, the idea of half-searching is used for reference. Multi-scale entropy correlation coefficient and frequency-domain correlation coefficient are proposed to improve the key links of VMD. The characteristics of frequency domain decomposition data are studied by simulation of bearing fault signal, and the essence of filtering is revealed. The simulation results of bearing fault signals and engineering application show that the improved VMD (IVMD) denoising effect is more obvious than that of the empirical mode decomposition (Empirical Mode) and the total average empirical mode decomposition (Ensemble Empirical Mode). It is an effective adaptive frequency domain mode decomposition method, which can extract the weak characteristic frequency information more accurately and realize the correct identification of bearing fault.
【作者單位】： 廣東石油化工學(xué)院計(jì)算機(jī)與電子信息學(xué)院;廣東石油化工學(xué)院廣東省石化裝備故障診斷重點(diǎn)實(shí)驗(yàn)室;華南理工大學(xué)自動化科學(xué)與工程學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61174113,61672174) 廣東省自然科學(xué)基金項(xiàng)目(2016A030307029)
【分類號】：TE65;TQ050.7
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本文編號：2164809