  A pattern recognition (PR) based diagnostic scheme is presented to identify
bearing faults, using time domain features. Vibration data is acquired from
faulty bearings using a test rig. The features are extracted from the data, and
processed prior to utilize in the PR process. The processing involves smoothing
of feature distributions. This reduces the undesired impact of vibration
randomness on the PR process, and thus enhances the diagnostic accuracy of the
model.
