本文選題：獨(dú)立成分分析 + 信息融合　； 參考：《機(jī)械科學(xué)與技術(shù)》2016年07期

【摘要】：峭度和負(fù)熵是盲信號(hào)獨(dú)立性的兩個(gè)自然測(cè)度,可以被用來(lái)捕捉機(jī)械振動(dòng)信號(hào)信息的動(dòng)態(tài)變化特征,并提取機(jī)械設(shè)備的故障特征信息。峭度和負(fù)熵是從兩個(gè)不同的角度和層面闡釋機(jī)械設(shè)備的故障特征信息,信息量是互補(bǔ)的。若將峭度信息和負(fù)熵信息融合,則必然能夠更全面、更深刻地來(lái)表征機(jī)械設(shè)備的狀態(tài)。因此引入信息融合的思想,提出基于ICA信息融合的機(jī)械故障特征信息提取方法,綜合峭度和負(fù)熵信息來(lái)提取機(jī)械設(shè)備的故障特征信息。液壓齒輪泵模式識(shí)別試驗(yàn)表明,該方法可以應(yīng)用于機(jī)械設(shè)備的故障特征信息提取。
[Abstract]:Kurtosis and negative entropy are two natural measures of blind signal independence, which can be used to capture the dynamic characteristics of mechanical vibration signal information and to extract the fault feature information of mechanical equipment. Kurtosis and negative entropy explain the fault feature information of mechanical equipment from two different angles and levels, and the amount of information is complementary. If the kurtosis information and the negative entropy information are fused, the state of mechanical equipment can be represented more comprehensively and profoundly. This paper introduces the idea of information fusion and proposes a method of extracting mechanical fault feature information based on ICA information fusion which combines kurtosis and negative entropy information to extract fault feature information of mechanical equipment. The pattern recognition test of hydraulic gear pump shows that this method can be used to extract fault feature information of mechanical equipment.
【作者單位】： 武警警官學(xué)院;第二炮兵工程大學(xué);武警工程大學(xué);
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61132008)資助
【分類號(hào)】：TP202;TH17

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