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The main objective is to identify potential faults using machine learning (ML) algorithms trained on harmonic features derived from stator current signals. Data acquisition was performed across three motor conditions: Healthy, Broken 1 Bar (BB1), and Broken 2 Bars (BB2), operating under three-phase current. The Fast Fourier Transform (FFT) technique was used to extract the 1st , 3rd, 5th, and 7th order harmonic components from each phase (R, S, T), generating twelve features per data sample. To ensure dataset reliability, a harmonic-based filtering method was applied by enforcing frequency thresholds at 250 Hz and 350 Hz to select valid Healthy samples. The processed data were then used to train three machine learning models: Linear Regression (LR), Random Forest (RF), and Decision Tree (DT). 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