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

2025-06-10

AI Summary

To address poor generalization of induction motor fault diagnosis models caused by scarce labeled fault data in industrial settings, this paper proposes SGDA—a physics-guided unsupervised frequency-domain data augmentation framework. SGDA requires no motor simulation; instead, it leverages intrinsic physical characteristics of current signals—particularly sideband harmonic structures—to synthesize physically interpretable, diverse, and realistic fault samples directly in the frequency domain. By deeply integrating Motor Current Signature Analysis (MCSA) with frequency-domain modeling, SGDA synergistically combines physical priors with data-driven learning. Experiments demonstrate that SGDA significantly improves classification accuracy and robustness for multi-class faults—including broken rotor bars and bearing defects—under low-data regimes. Its effectiveness and deployment feasibility are validated on real-world industrial datasets.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Enhancing induction motor diagnostics using ML and physics models
• Generating realistic motor faults without intensive simulations
• Improving diagnostic accuracy with unsupervised signature-guided augmentation

Innovation

• Signature-Guided Data Augmentation for motor diagnostics
• Unsupervised anomaly generation using engine physics
• Hybrid ML and signature analysis for accuracy