本文選題：軸承故障診斷　切入點(diǎn)：經(jīng)驗(yàn)小波變換　出處：《電子測(cè)量與儀器學(xué)報(bào)》2017年08期 　論文類型：期刊論文

【摘要】：實(shí)際應(yīng)用中研究機(jī)械系統(tǒng)的工作狀態(tài)時(shí),通常會(huì)對(duì)其所產(chǎn)的信號(hào)進(jìn)行研究分析,從而得出相關(guān)結(jié)論。這些由機(jī)械系統(tǒng)產(chǎn)生的信號(hào)一般含有多種不同波動(dòng)的混合成分,為了得出可靠的結(jié)論,必須從復(fù)合信號(hào)和背景噪聲中分離出有物理意義的成分。因此引入一種新的故障提取方法,首先利用一種較新的模態(tài)分解算法——經(jīng)驗(yàn)小波變換,將一組信號(hào)分解成多個(gè)具有緊支撐傅里葉頻譜的調(diào)幅-調(diào)頻(AM-FM)分量;然后利用K-L散度值挑選出具有物理意義的分量;最后將挑選出的分量通過(guò)三點(diǎn)對(duì)稱差分能量算子運(yùn)算,得到其能量譜的同時(shí)也能得到瞬時(shí)頻率,從而提取出故障特征。將該方法用于模擬信號(hào)和實(shí)際軸承故障信號(hào),并且同之前的方法進(jìn)行對(duì)比。結(jié)論表明,該方法不僅能很好的提取軸承故障特征,而且證明該方法具有更好的優(yōu)越性。
[Abstract]:When studying the working state of a mechanical system in practical application, the signals produced by the mechanical system are usually studied and analyzed, and the relevant conclusions are drawn. These signals produced by the mechanical system generally contain a variety of mixed components with different fluctuations. In order to get a reliable conclusion, the physical components must be separated from the composite signal and background noise. Therefore, a new fault extraction method is introduced. Firstly, a new mode decomposition algorithm, empirical wavelet transform, is used. A set of signals is decomposed into several amplitude-frequency modulation (AM FM) components with compact support Fourier spectrum, and then the physical components are selected by using K-L divergence value. Finally, the selected components are calculated by a three point symmetric differential energy operator. At the same time, the instantaneous frequency can be obtained, and the fault characteristics can be extracted. The method is used to simulate the signal and the actual bearing fault signal, and compared with the previous method. The conclusion shows that, This method can not only extract the bearing fault features well, but also prove that this method has better advantages.
【作者單位】： 長(zhǎng)安大學(xué)道路施工技術(shù)與裝備教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：中央高校教育教學(xué)改革專項(xiàng)經(jīng)費(fèi)建設(shè)項(xiàng)目(jgy16049,0012-310600161000)資助
【分類號(hào)】：TH133.3

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