Induction Motor Bearing Fault Diagnosis Using Hilbert-Based Bispectral Analysis

This paper addresses the development of a new signal processing approach based on the fusion of Hilbert transform and bispectral analysis to extract features of defects in a number of induction motor bearing conditions. The bearing conditions considered are a normal bearing and bearings with outer and inner race faults. The signal processing techniques based on Hilbert transform have been used to extract the modulating components which are able to characterize the bearing fault patterns. The use of bispectral analysis provides great capabilities for detection and characterization of nonlinearity in the bearing systems. Experiment results show that the proposed approach, called the Hilbert-based bispectral analysis, is capable of completely extracting the characteristic frequencies related to the defect from the resonant frequency band.