基于圆域分析的大型回转支承初期故障诊断

发布时间：2018-05-26 14:19

本文选题：回转支承 + 圆域分析　；参考：《振动与冲击》2017年09期

【摘要】：为解决真实工况下大型回转支承振动信号背景噪声大、常用故障诊断方法难以适用的问题,提出了一种基于圆域分析的振动信号处理方法。将时域信号进行圆域转换,并按一定角度将转换后的圆域信号划分成多个区域;判断各区域信号邻域相关离散点拟合椭圆的倾角方向,得到回转支承整圈对应的多个异常向量;以异常向量的平均向量作为圆域分析的特征向量,分析其均值、方差、歪度和峭度指标的变化情况,实现回转支承的故障诊断。对某型号回转支承进行了加速寿命试验,结果表明,该方法能够有效诊断出回转支承滚道的区域滑移、点蚀等初期故障,相比常见的时域特征、小波分析等方法准确度更高,故障可识别度更强,因此可以用于实际工况下回转支承的故障诊断。
[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.
【作者单位】：南京工业大学机械与动力工程学院;
【基金】：国家自然科学基金项目(51375222;51175242)
【分类号】：TH17

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