發(fā)布時(shí)間：2018-05-19 16:49

  本文選題：故障程度評(píng)估 + 視情維修�。� 參考：《機(jī)械科學(xué)與技術(shù)》2016年08期

【摘要】：提出了一種基于AR-GMM的滾動(dòng)軸承故障程度評(píng)估方法,該方法利用自回歸模型(AR)提取無故障軸承早期振動(dòng)信號(hào)特征,并建立無故障軸承高斯混合模型(GMM)作為故障程度評(píng)估基準(zhǔn)。軸承后期振動(dòng)信號(hào)在提取AR特征后導(dǎo)入該基準(zhǔn)GMM模型,得到測(cè)試樣本與無故障樣本之間的量化相似程度。進(jìn)而以此相似程度值為基礎(chǔ)建立自回歸對(duì)數(shù)似然概率值(ARLLP)作為滾動(dòng)軸承故障程度評(píng)估指標(biāo)。軸承疲勞試驗(yàn)分析表明該指標(biāo)能夠及時(shí)有效發(fā)現(xiàn)軸承早期故障,并能很好預(yù)測(cè)跟蹤軸承惡化趨勢(shì),為視情維修奠定基礎(chǔ)。
[Abstract]:A fault degree evaluation method for rolling bearings based on AR-GMM is proposed. The autoregressive model (ARM) is used to extract the early vibration signals of fault free bearings, and a hybrid Gao Si model for fault free bearings is established as a benchmark for fault degree evaluation. After extracting the AR feature, the vibration signal of the bearing is imported into the benchmark GMM model, and the quantitative similarity between the test sample and the fault free sample is obtained. On the basis of the similarity value, an autoregressive logarithmic likelihood probability (ARLLP) is established as an index to evaluate the fault degree of rolling bearing. The analysis of bearing fatigue test shows that this index can detect the early failure of bearing in time and effectively, and can predict and track the deterioration trend of bearing well, and lay the foundation for maintenance according to the situation.
【作者單位】： 華東交通大學(xué)機(jī)電與車輛工程學(xué)院;
【基金】：國家自然科學(xué)基金資目(51265010;51205130) 江西省自然科學(xué)基金項(xiàng)目(20161BAB216134) 載運(yùn)工具與裝備教育部重點(diǎn)實(shí)驗(yàn)室項(xiàng)目(15JD02)資助
【分類號(hào)】：TH133.33
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本文編號(hào)：1910888