本文選題：回轉支承 + 圓域分析��； 參考：《振動與沖擊》2017年09期

【摘要】：為解決真實工況下大型回轉支承振動信號背景噪聲大、常用故障診斷方法難以適用的問題,提出了一種基于圓域分析的振動信號處理方法。將時域信號進行圓域轉換,并按一定角度將轉換后的圓域信號劃分成多個區(qū)域;判斷各區(qū)域信號鄰域相關離散點擬合橢圓的傾角方向,得到回轉支承整圈對應的多個異常向量;以異常向量的平均向量作為圓域分析的特征向量,分析其均值、方差、歪度和峭度指標的變化情況,實現回轉支承的故障診斷。對某型號回轉支承進行了加速壽命試驗,結果表明,該方法能夠有效診斷出回轉支承滾道的區(qū)域滑移、點蝕等初期故障,相比常見的時域特征、小波分析等方法準確度更高,故障可識別度更強,因此可以用于實際工況下回轉支承的故障診斷。
[Abstract]:In order to solve the problem that the background noise of vibration signal of large slewing bearing is large and the common fault diagnosis method is difficult to apply in real working condition, a vibration signal processing method based on circle analysis is proposed. The time domain signal is converted in circle domain, and the transformed circle signal is divided into several regions according to certain angle, and the relative discrete points of each region signal neighborhood fit the obliquity direction of the ellipse, and many abnormal vectors corresponding to the whole circle of the slewing support are obtained. The mean vector of abnormal vector is used as the eigenvector of circle domain analysis, and the variation of mean value, variance, skew degree and kurtosis index is analyzed, and the fault diagnosis of slewing bearing is realized. The accelerated life test of a certain type of slewing bearing shows that the method can effectively diagnose the initial faults such as slip and pitting of the raceway of the slewing bearing, and the wavelet analysis method is more accurate than the common time domain features. It can be used in the fault diagnosis of slewing bearing under actual working condition.
【作者單位】： 南京工業(yè)大學機械與動力工程學院;
【基金】：國家自然科學基金項目(51375222;51175242)
【分類號】：TH17

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本文編號：1937541