基于圆域分析的大型回转支承初期故障诊断

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。

Incipient fault diagnosis of large-size slewing bearings based on circular domain analysis

The background noise of a slewing bearing vibration signal in practical load cases is very high,it makes commonly used fault detection approaches not suitable for slewing bearing fault diagnosis.Therefore,a novel signal processing method was proposed based on circular domain analysis.First of all,the time domain signal was transformed into a circular domain and the transformed signal was divided into several zones according to a certain angle,and then the neighborhood correlation discrete points of each zone were fitted as an ellipse.Afterwards,the ellipses skewing to the right were tagged as abnormalities and the corresponding abnormal vectors were obtained based on the whole cycle of a slewing bearing.Finally,the characteristic vector of circular domain analysis,also the mean vector of all the abnormal vectors was acquired,and its mean,variance,skewness and kurtosis were calculated and taken as the fault indicators.An accelerated life test was conducted on a slewing bearing to validate the proposed method.Results showed that the proposed method has a better performance to detect an incipient fault,such as,slipping and pitting in the raceway than the time domain analysis and the wavelet analysis do,it can be an effective tool for slewing bearing fault diagnosis in engineering practice.