A Novel Enhancement and Tracking Extraction Method of Bearing Fault Features for Rotating Machinery

The rolling bearing is one of the most important parts of rotating machinery. In the early stage of weak impact or the background of strong noise, the extraction of the fault features would become difficult. The extraction result of fault characteristics plays an important role in the accuracy of fault diagnosis. A novel enhancement and tracking extraction method combining minimum entropy deconvolution (MED) and Vold-Kalman filter (VKF) is presented in this manuscript. Experimental investigation of the 6205-2RS JEM SKF bearing with inner ring defects is performed. The experimental results prove that, by using the proposed method, the amplitude of the fault feature is almost twice as much as the healthy characteristic. The results show that the proposed extraction method can provide an excellent solution for fault diagnosis of rolling bears.