Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics
This work addresses the need for more efficient and accurate fault diagnosis in industrial motors, though it appears incremental as it combines existing ML techniques with signature analysis.
The paper tackled the problem of diagnosing faults in three-phase induction motors by proposing Signature-Guided Data Augmentation (SGDA), a method that synthesizes physically plausible fault data in the frequency domain, achieving superior diagnostic accuracy and reliability for industrial applications.
The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy. Traditional methods largely rely on signature analysis, which, despite being a standard practice, can benefit from the integration of advanced ML techniques. In our study, we innovate by combining ML algorithms with a novel unsupervised anomaly generation methodology that takes into account the engine physics model. We propose Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals. Guided by Motor Current Signature Analysis, SGDA creates diverse and realistic anomalies without resorting to computationally intensive simulations. This hybrid approach leverages the strengths of both supervised ML and unsupervised signature analysis, achieving superior diagnostic accuracy and reliability along with wide industrial application. The findings highlight the potential of our approach to contribute significantly to the field of engine diagnostics, offering a robust and efficient solution for real-world applications.