New health indicators for the monitoring of bearing failures under variable loads

Abstract

Bearings are one of the most critical components in rotating machines. Unexpected failure of this components may cause serious damages and unplanned breakdowns. In this paper, a new method is proposed for bearing fault diagnosis under various loads based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the sequential backward selection (SBS). The CEEMDAN is used for the automatic selection of intrinsic mode functions from vibration signals to build a bank of health indicators. Then, the SBS algorithm is used to select the most relevant indicators of the bearing different failure modes. To test the proposed method accuracy, it was first applied to the Case Western Reserve University dataset. Then, data collected from a dedicated test bench of the Laboratory of Signal and Industrial Process Analysis were introduced to this method to classify different bearing health states. The obtained results show that the proposed method is effective in identifying and classifying bearing faults under various loads with high accuracy. This method can be used for condition monitoring of bearings and prognostics in real industrial applications.