Sound analysis to diagnosis inner race bearing damage on induction motors using fast fourier transform

The induction motor is a type of electric machine that is widely used for industrial operations in this modern era. It is an alternating current electric machine with several advantages, namely cheap, simple construction, and not requiring excessive maintenance, but has the biggest percentage of motor fault in the bearings. Therefore, this study aims to identify the inner race-bearing fault detection system based on sound signal frequency analysis. The sound signal processing was carried out using the Fast Fourier Transform (FFT) algorithm to analyze the condition of the inner race-bearing. The sound signal was used because it does not require direct contact with the bearing (non-invasive). The fault detection system was tested with two defects, namely scratched inner race and perforated inner race bearing. The results gave a successful detection of the condition of the inner race bearing with a percentage of 81.24%. This showed that the fault detection system using sound signals with FFT signal processing was carried out with high accuracy.