New Time Domain Method for the Detection of Roller Bearing Defects

The main focus of this paper is given to the detection of different fault types in the inner or outer race of roller bearing. The first group covers faults with an extension beyond the spacing between two rolling elements. In these cases some of the classical methods like envelop techniques could fail. We show that the vibration structure generated by a rough surface differs from a normal state even if the signal energy is eliminated by normalisation of the data. Suitable time domain features are a mathematical description of the shape of selected time domain peaks, which could easily be calculated by the higher derivatives of the time acceleration signal and some parameters characterizing the randomness of the peak positions. After the step of extracting 30 features from the time signal a feature selection process is executed automatically. This enables the selection of a feature subset which is best suited to the present fault situation. As a second group we start investigations for the early detection of very small bearing damages like false brinelling faults, which occur in the presence of a small relative motion between the rollers and raceways during non-rotation times. This leads to a damage which is characterized by elliptical wear marks in the axial direction at each roller position. Some test rig results indicate the high potential of the new time domain features for both fault types