A Signal Spectrum Based Fault Diagnosis of Induction Motor Based on Low Sampling Rate

In this study, we present an advanced methodology for the fault diagnosis of Induction Motor (IM) focusing on detecting broken rotor bar (BRB) faults at a reduced sampling rate while maintaining diagnostic accuracy. While effective, traditional high sampling rate data acquisition techniques pose significant challenges including high memory requirements, increased data loss risk, and substantial need for a powerful processor to handle the large data, thus driving up the measurement process’s overall cost. To address these challenges, we introduce the Integral Number of Cycles (INOC) approach integrated with the Fast Fourier Transform (FFT) process along with the concept of multi-rate sampling to enhance spectral analysis of machine signals, focusing on mitigating spectral leakage. This novel method effectively eliminates the need for windowing functions, simplifying the spectral analysis process. Our research demonstrates that data acquisition at a low sampling rate of 2 kHz is sufficient for accurate fault detection. Comparative analysis between the proposed FFT after INOC and the conventional FFT with Hann Window shows that our method yields comparable results, providing sharper spectral representations without windowing. The analysis of results is performed using experimental data under various loading conditions of IM.