<ul><li>Machine learning algorithms can enhance diagnostic performance of three-phase engines by combining with traditional signature analysis.</li><li>A novel unsupervised anomaly generation methodology called Signature-Guided Data Augmentation (SGDA) is proposed to synthesize realistic faults in healthy current signals.</li><li>SGDA leverages Motor Current Signature Analysis and creates diverse anomalies in the frequency domain without the need for complex simulations, improving diagnostic accuracy and reliability.</li><li>This hybrid approach shows promise in the field of engine diagnostics, providing a robust and efficient solution for industrial applications.</li></ul>

Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics

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