Accurate and reliable fault diagnosis of three-phase induction motors is crucial for enhancing industrial reliability and reducing unplanned shutdown, particularly in variable frequency drive (VFD)–fed systems where signal distortion is considerable. This paper suggests a hybrid and intelligent framework that fuses vibration and current signal that integrates Discrete Wavelet Transform (DWT), statistical feature extraction, and a Dual-branch one-dimensional Convolutional Neural Network (1D-CNN) for the diagnosis and classification of mechanical and electrical induction motor faults. To isolate fault-sensitive frequency bands while mitigating VFD-induced noise, vibration, and stator current signals are decomposed using a seven-level DWT. Discriminative statistical features are extracted from the selected sub-bands and ranked using Information Gain to reduce feature dimensionality and computational complexity. Using a dual-branch 1D-CNN architecture, the optimized feature set is then classified. The suggested method is validated experimentally under various load and speed conditions through nine motor health states, including healthy operation, bearing faults, stator winding short circuits, and broken rotor bars. The results establish a high classification accuracy of 99.4% and a weighted F1-score of 0.994, with minimal misclassification among fault categories. These findings verify the effectiveness, robustness, and expandability of the suggested vibration–current fusion framework, spotlighting its suitability for real-time condition monitoring in intelligent manufacturing environments.

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