Fault diagnosis of roller bearing using feedback EMD and decision tree

Abstract

This paper proposed a method for roller bearing fault diagnosis using Empirical Mode Decomposition (EMD) algorithm and decision tree. First, to obtain the Intrinsic Mode Functions (IMFs) of bearing vibration signal processed by EMD and processing the IMFs with autocorrelation for noise elimination, then extract the principal frequency as features in frequency domain. Second, build up decision tree with C4.5 algorithm and forecast samples. Feedback the node attributes information to EMD for reducing the calculation of needless attribute and optimizing signal processing algorithm. The experimental bearing vibration signal in accordance with the following conditions: normal, inner race fault, outer race fault and balls fault. The experiment result illustrated that the correct ratio of diagnosis is high and efficient. The method proposed is high accurate and useful for safe production.