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.