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

TL;DR

This paper introduces Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes realistic faults in engine signals to improve machine learning-based diagnostics of three-phase motors.

Contribution

It combines ML with physics-based anomaly generation to enhance diagnostic accuracy without intensive simulations.

Findings

SGDA creates diverse, physically plausible faults in frequency domain signals.

The hybrid approach improves diagnostic accuracy and reliability.

Applicable to real-world industrial engine diagnostics.

Abstract

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.