Feature-Based Bearing Fault Classification Using Taylor-Fourier Transform

This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor-Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in the spurious current signal components, wherewith it is possible to gain information to detect bearing issues and classify them using statistical methods. The methodology was implemented in MATLAB using the digital Taylor-Fourier transform for three fault types (bearing ball damage, outer-race damage, and corrosion damage) at different powering conditions: power grid source at 60 Hz and adjustable speed drive applied (60 Hz, 50 Hz, 40 Hz, 30 Hz, 20 Hz, and 10 Hz) in loading and unloading conditions. Results demonstrate a classification accuracy between 93-99% for bearing ball damage, 91-99% for outer-race damage, and 94-99% for corrosion damage.