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 This paper presents a bearing fault detection method for permanent magnet synchronous motors (PMSMs). Owing to its good characteristics, PMSM usage has increased; however, a substantial number of motor failures are also reported. Many studies have focused on bearing fault detection using vibration sensors. However, current sensors are already installed in many industries, and therefore, if bearing faults can be detected using these sensors, there would be no need to install additional sensors. A frequency analysis is performed to detect bearing faults and fast Fourier transform (FFT)-based methods can be used for the same. FFT needs to have a high resolution to be able to differentiate between the frequencies of bearing faults from those of the stator current. However, FFT requires extensive data and high computational cost to achieve this high resolution. Therefore, the zoom FFT (ZFFT) algorithm is implemented to minimize the computational cost and to increase the resolution. The experimental results verify the effectiveness of the proposed method by comparing FFT and ZFFT waveforms.