SGDA generates synthetic faults in the frequency domain from healthy signals to augment training data for ML-based induction motor diagnostics, claiming superior accuracy.
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Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics
SGDA generates synthetic faults in the frequency domain from healthy signals to augment training data for ML-based induction motor diagnostics, claiming superior accuracy.