Abstract
Introduction. Bearing faults in induction motors are one of the primary causes of performance degradation and unexpected failures in industrial systems. Early fault detection remains challenging because conventional protection systems generally respond only after severe damage occurs. In addition, motor current signals exhibit nonlinear and complex characteristics, requiring advanced analysis techniques for accurate fault identification. Problem. Existing fault diagnosis methods often suffer from limited classification accuracy, dependency on specific operating conditions, and insufficient integration between spectral feature extraction and adaptive classification techniques. Goal. To develop a non-invasive bearing fault classification method based on current spectrum analysis and artificial neural network (ANN) for induction motor condition monitoring. Methodology. The proposed method utilizes fast Fourier transform (FFT) to transform motor current signals from the time domain into the frequency domain for spectral feature extraction. The extracted features are then processed using principal component analysis (PCA) for dimensionality reduction before being used as inputs to the ANN classifier. Experimental testing is conducted under 3 bearing conditions, namely normal, 7-ball fault, and 6-ball fault conditions, using 50 datasets for each condition. Results. The results demonstrate that the proposed method successfully identifies bearing conditions with high classification accuracy and strong separation characteristics in the PCA space. FFT analysis also reveals consistent spectral changes corresponding to fault severity, particularly in sideband components and energy distribution patterns. Scientific novelty. This work integrates FFT-based current spectrum analysis, PCA-based feature reduction, and ANN classification into a unified non-invasive diagnosis framework for bearing fault detection. Practical value. The proposed approach provides a simple, adaptive, and reliable solution for early bearing fault detection without requiring additional mechanical sensors, making it suitable for industrial condition monitoring applications. References 32, tables 4, figures 7.