Application of Short Time Fourier Transform (STFT) For Diagnosing Rolling Bearing Faults

Abstract

A fan is crucial for maintaining airflow in industries. Bearings in fans prevent friction and must be robust to function effectively. Damage to the bearings can diminish machine performance. Predictive maintenance is essential for early detection of faults. One way to analyze bearing faults is by using the Short Time Fourier Transform (STFT), as it excels in analyzing non-stationary signals. Experiments were conducted under normal conditions and with inner race faults in bearings at a shaft speed of 1162.5 Hz. Vibration detection was done using an accelerometer sensor, and Matlab analysis was employed. The data was processed using the Fourier Transform (FT) method through both time and frequency domains, as well as the STFT through spectrograms. In the spectrum plot, there is still a significant amount of noise present. This high amplitude of noise from other frequencies obscures the bearing fault amplitudes. Furthermore, the Fourier Transform (FT) is only suitable for analyzing stationary signals. To address this, an envelope analysis was used to filter out the noise. The STFT analysis method provides simultaneous frequency and time information. This reveals that the spectrogram results for inner race faults depict three high amplitude peaks at harmonic frequencies. This indicates that the signal is non-stationary due to fluctuating amplitudes, making bearing fault analysis more accessible.