本文選題：局部均值分解 + 噪聲統(tǒng)計特性��； 參考：《中國測試》2016年06期

【摘要】：工程實際中測得的滾動軸承信號往往含有大量的噪聲,這使得軸承故障特征淹沒在噪聲中難以被提取。針對這一問題,提出一種基于隨機噪聲統(tǒng)計特性與局部均值分解(local mean decomposition,LMD)理論相結(jié)合的滾動軸承故障診斷方法。首先,利用LMD將原信號分解,得到若干乘積函數(shù)(production function,PF)分量;其次,將第一階PF分量隨機排序,與剩余PF分量相加;然后,對第2步進(jìn)行P次循環(huán),求平均;最后,把第3步得到的信號作為原信號,重復(fù)第1、2步Q次,對得到的信號進(jìn)行頻譜分析,提取故障特征。通過對仿真信號和實驗臺軸承實驗信號進(jìn)行分析研究表明,該方法可準(zhǔn)確診斷滾動軸承元件故障,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)機械工程學(xué)院;
【基金】：國家自然科學(xué)基金項目(21366017) 內(nèi)蒙古科技廳應(yīng)用與研究開發(fā)計劃項目——高新技術(shù)領(lǐng)域科技計劃重大項目(20130302)
【分類號】：TH133.33

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本文編號：2029106