Fuzzy-based Induction Motor Fault Diagnosis Decision-Making System for Motor Current Signature Analysis

Motor Current Signature Analysis (MCSA) is a leading approach for detecting induction motor faults such as eccentricity, stator, and rotor issues. This paper explores integrating Fast Fourier Transform (FFT) and Wavelet Transform (WT) into MCSA to identify irregularities in current signals and provide fault information. While FFT offers faster analysis, its fault insights are limited. To address this, a time-efficient MCSA method with fuzzy logic decision-making is introduced, evaluating motor load and comparing time domain current signals to a healthy motor baseline. The method yields three outputs: Normal, FFT, and WT, optimizing fault analysis based on specific motor conditions. Validation in MATLAB/SIMULATION demonstrates improved runtime without compromising accuracy, offering a promising real-time monitoring solution for industrial induction motors, balancing accuracy and computational speed.