Importance of the fourth and fifth intrinsic mode functions for bearing fault diagnosis

In the most of industrial and domestic applications bearings present important assets. The diagnostic of these elements needs accurate and reliable acquisition of its dynamic vibration signals affected by noise and other part of system such as gears, bars... Empirical Mode Decomposition (EMD) is a new signal processing method used to decompose non-stationary and non-linear vibration bearing signals into several stationary empirical mode components called Intrinsic Mode Functions (IMF). For each IMF, the energy entropy mean is computed. This technique is compared to the most used statistical features (RMS, Kurtosis) using a characterization degree. Experimental results show that time domain feature extraction is effective for bearing fault feature extraction as type (inner race, outer race, rolling element) and severity (normal, degraded, faulting). The choice of the most significant IMFs is also discussed in this paper.