GNN-ASE: Graph-Based Anomaly Detection and Severity Estimation in Three-Phase Induction Machines

TL;DR

This paper introduces a model-free Graph Neural Network approach for fault diagnosis in induction machines, effectively detecting multiple fault types directly from raw signals with high accuracy, simplifying traditional diagnostic processes.

Contribution

The paper presents a novel GNN-based framework that automatically learns features from raw data for fault detection in induction machines, eliminating the need for complex modeling or manual feature extraction.

Findings

Achieved over 92% accuracy in fault detection

Effective across different fault types and load conditions

Simplifies diagnosis without signal preprocessing

Abstract

The diagnosis of induction machines has traditionally relied on model-based methods that require the development of complex dynamic models, making them difficult to implement and computationally expensive. To overcome these limitations, this paper proposes a model-free approach using Graph Neural Networks (GNNs) for fault diagnosis in induction machines. The focus is on detecting multiple fault types -- including eccentricity, bearing defects, and broken rotor bars -- under varying severity levels and load conditions. Unlike traditional approaches, raw current and vibration signals are used as direct inputs, eliminating the need for signal preprocessing or manual feature extraction. The proposed GNN-ASE model automatically learns and extracts relevant features from raw inputs, leveraging the graph structure to capture complex relationships between signal types and fault patterns. It is evaluated for both individual fault detection and multi-class classification of combined fault conditions. Experimental results demonstrate the effectiveness of the proposed model, achieving 92.5\% accuracy for eccentricity defects, 91.2\% for bearing faults, and 93.1\% for broken rotor bar detection. These findings highlight the model's robustness and generalization capability across different operational scenarios. The proposed GNN-based framework offers a lightweight yet powerful solution that simplifies implementation while maintaining high diagnostic performance. It stands as a promising alternative to conventional model-based diagnostic techniques for real-world induction machine monitoring and predictive maintenance.