Bearing fault diagnosis using a spectral average based approach

The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. In this paper, a new bearing fault diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals is presented. In this method, the accelerometer signal is first digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data to compute spectral average and extract condition indicators for bearing fault diagnosis. The method is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage. Copyright