VAE-based feature extraction for SVM classification of rotor broken bar faults in induction motors

Broken-bar faults in induction motors can degrade performance and compromise reliability. Conventional fault diagnosis methods based on time- and frequency-domain feature extraction require expert knowledge and extensive manual processing, limiting their real-time applicability. This work proposes a framework combining Variational Autoencoders (VAE) and Support Vector Machines (SVM) for automatic fault diagnosis with severity assessment. The VAE extracts informative latent features from high-dimensional vibration signals while performing nonlinear dimensionality reduction. These features are then used to train an SVM to classify normal and broken rotor bar fault severity levels operating modes. The method was validated on a publicly available dataset collected from a 1 HP induction motor under various load and rotor fault conditions. Results demonstrate that the VAE-SVM framework achieves high classification accuracy and reliably discriminates between vibration signal classes across multiple sensor placements under the considered experimental conditions. These results suggest that the proposed approach is a promising framework for automatic fault diagnosis, with potential applications in condition monitoring and predictive maintenance. Further validation under more diverse operating conditions and real industrial environments is required to confirm its robustness and practical applicability.