Rolling element bearing fault classification using soft computing techniques

This paper presents a method, based on classification techniques, for automatically detecting and diagnosing various types of defects which may occur on a rolling element bearing. In the experiments we have used vibration signals coming from a mechanical device including more than ten rolling element bearings monitored by means of four accelerometers: the signals have been collected both with all faultless bearings and substituting one faultless bearing with an artificially damaged one: four different defects have been taken into account. The proposed technique considers all the aspects of classification: feature selection, different base classifiers (two statistical classifiers, namely LDC and QDC, and MLP neural networks) and classifier fusion. Experiments, performed on the vibration signals represented in the frequency domain, have shown that the proposed classification method is highly sensitive to different types of defects and to different severity degrees of the defects.