Automatic diagnosis of defects of rolling element bearings based on computational intelligence techniques

This paper presents a method, based on classification techniques, for automatic detection and diagnosis of defects of rolling element bearings. We used vibration signals recorded by four accelerometers on a mechanical device including rolling element bearings: the signals were collected both with all faultless bearings and after substituting one faultless bearing with an artificially damaged one. We considered four defects and, for one of them, three severity levels. In all the experiments performed on the vibration signals represented in the frequency domain we achieved a classification accuracy higher than 99%, thus proving the high sensitivity of our method to different types of defects and to different degrees of fault severity. We also assessed the degree of robustness of our method to noise by analyzing how the classification performance varies on variation of the signal-to-noise ratio and using statistical classifiers and neural networks. We achieved very good levels of robustness.