Rolling Bearing Fault Diagnosis with EMD-Based Fault Characteristic Frequency Difference Analysis

This paper presents a novel single-point rolling bearing fault diagnosis mechanism through vibration signal analysis. It is highlighted that the rolling bearing operational state can be well estimated by the first small set of Intrinsic Mode Function (IMF) components of the original vibration measurements through Empirical Mode Decomposition (EMD). These IMF components can be further translated into envelope spectrum by using Hilbert Transform. As a result, the difference of fault characteristic frequencies (DFCF) is derived to properly characterize different fault patterns for fault diagnosis. The suggested method is implemented and evaluated in a rolling bearing test bed for a range of failure scenarios (e.g. inner and outer raceway fault, rolling elements fault) with extensive vibration measurements. The result demonstrates that the proposed solution is effective for characterizing and detecting arrange of rolling bearing faults.quality).