本文選題：局部均值分解 + 噪聲統(tǒng)計(jì)特性��； 參考：《中國(guó)測(cè)試》2016年06期

【摘要】：工程實(shí)際中測(cè)得的滾動(dòng)軸承信號(hào)往往含有大量的噪聲,這使得軸承故障特征淹沒(méi)在噪聲中難以被提取。針對(duì)這一問(wèn)題,提出一種基于隨機(jī)噪聲統(tǒng)計(jì)特性與局部均值分解(local mean decomposition,LMD)理論相結(jié)合的滾動(dòng)軸承故障診斷方法。首先,利用LMD將原信號(hào)分解,得到若干乘積函數(shù)(production function,PF)分量;其次,將第一階PF分量隨機(jī)排序,與剩余PF分量相加;然后,對(duì)第2步進(jìn)行P次循環(huán),求平均;最后,把第3步得到的信號(hào)作為原信號(hào),重復(fù)第1、2步Q次,對(duì)得到的信號(hào)進(jìn)行頻譜分析,提取故障特征。通過(guò)對(duì)仿真信號(hào)和實(shí)驗(yàn)臺(tái)軸承實(shí)驗(yàn)信號(hào)進(jìn)行分析研究表明,該方法可準(zhǔn)確診斷滾動(dòng)軸承元件故障,具有有效性。
[Abstract]:In engineering practice, the rolling bearing signals often contain a lot of noise, which makes it difficult to extract the bearing fault characteristics in the noise. In order to solve this problem, a rolling bearing fault diagnosis method based on the statistical characteristics of random noise and the local mean decomposition (LMD) theory is proposed. First, the original signal is decomposed by LMD, and some product functions are obtained. Secondly, the first order PF component is sorted randomly with the remaining PF component. Then, the second step is cycled P to get the average. The signal obtained in step 3 is taken as the original signal and the second step Q is repeated. The frequency spectrum of the obtained signal is analyzed and the fault feature is extracted. Through the analysis of the simulation signal and the experimental signal of the bearing, it is shown that the method can accurately diagnose the fault of the rolling bearing element, and it is effective.
【作者單位】： 內(nèi)蒙古科技大學(xué)機(jī)械工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(21366017) 內(nèi)蒙古科技廳應(yīng)用與研究開發(fā)計(jì)劃項(xiàng)目——高新技術(shù)領(lǐng)域科技計(jì)劃重大項(xiàng)目(20130302)
【分類號(hào)】：TH133.33

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本文編號(hào)：2029106