Use of empirical mode decomposition and K- nearest neighbour classifier for rolling element bearing fault diagnosis

Abstract

Rolling bearing is widely used in rotary machine; the unexpected failure affects the normal operation of the machine leads to production and financial losses to the industry. Hence, Rolling Element Bearing (REB) fault diagnosis is crucial. In this paper, raw bearing vibration signal is divided into the various intrinsic mode functions (IMFs) using Empirical Mode Decomposition (EMD). Statistical features are extracted from the first three IMFs of each conditions of the bearing. These features were given as inputs to K-NN classifier to classify different condition of the bearing.