Application of fourier transform for early detection of bearing failures in electric motors - Giovanni Dimas Prenata; Muhammad Alif Sofi Syariffudin; Reza Sarwo Widagdo (Geuthèë Institute, 2026)
This study presents an analysis of bearing fault conditions in electric motors through stator current measurements and their transformation into the frequency domain. Measurements were conducted under two main bearing conditions: normal and damaged, each tested with three load variations (no load, generator load, and generator load with one lamp). The time-domain current waveforms showed minimal visual distinction between normal and damaged bearing conditions, making classification difficult. Therefore, the current data were transformed into the frequency domain using the Discrete Fourier Transform (DFT). The frequency domain analysis revealed that in normal bearing conditions, the frequency magnitude distribution was relatively stable and symmetrical, with low fluctuation in the frequency index range k = 0 to k = 10. In contrast, damaged bearing conditions exhibited larger and irregular fluctuations in frequency magnitude across different load levels, indicating a distinct signature of bearing failure. Consequently, frequency domain analysis proves to be an effective approach for detecting bearing faults based on the spectral characteristics of motor current signals.