On various specialized vibration techniques for detection of bearing faults

Detection of bearing faults from raw time domain or frequency domain data is extremely difficult in view of the fact that there are a very large number of frequency components present in these signals. For this reason, other specialized signal processing techniques have been tried out. Experiments were carried out on a specially fabricated bearing test rig with variable speed drive and hydraulic loading arrangement. Faults were induced in an SKF N 307 roller bearing and the bearing housing vibrations were sensed by piezoelectric accelerometers. These signals were fed to a personal computer through a Data Acquisition System (DAS) and processed using MATLAB software. The enveloping spectrum, cepstrum and the auto regressive model based spectrum have been presented for the case of bearings with inner race (IR) and outer race (OR) defects. These results prove that the above methods have an edge over conventional methods for fault diagnosis.