Hybrid Fault Detection in Three-Phase Induction Motors

P. 357–360.

Ali S., Khizhik A., Ryzhikov A., Derkach D., Svirin S.

Three-phase induction motors play a crucial role in industrial applications due to their efficiency, durability, and reliability. However, effective fault detection remains challenging, primarily due to the scarcity of labeled failure data, which limits the performance of traditional machine learning (ML)-based diagnostic models and increases the risk of overfitting and poor generalization. Conventional methods, such as current signature analysis (CSA), have long been used for motor diagnostics, but can be further enhanced by integrating advanced ML techniques. To address these challenges, we propose a hybrid approach that combines CSA with a ResNet-based deep learning model, incorporating a physically informed synthetic anomaly generation process. This method leverages the predictive capabilities of supervised ML while maintaining the diagnostic robustness of unsupervised signature analysis, resulting in higher accuracy and improved generalization in different motor conditions. Experimental evaluations demonstrate that our approach outperforms traditional ML diagnostic techniques, making it an effective solution for industrial applications. The findings underscore the potential impact of this method in development of intelligent fault detection systems, paving the way for more reliable and automated predictive maintenance strategies in industrial settings.

Keywords: signature analysis, fault detection, Support Vector Machines (SVM), Machine learning, Induction Motor Fault Detection

In book

2025 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), 12-13 May 2025

IEEE, 2025.