Real-Time Detection of Broken Rotor Bar Faults in Three-Phase Induction Motors Using ResNet-50 and Siemens S7-315 PLC

This paper presents a novel approach for real-time detection of broken rotor bar (BRB) faults in three-phase induction motors, leveraging the deep learning capabilities of ResNet-50 and the industrial robustness of Siemens S7-315 PLCs. Traditional diagnostic methods, such as Motor Current Signature Analysis (MCSA) and time-frequency analysis, often falter in dynamic environments due to noise, fluctuating loads, and complex fault scenarios. To address these limitations, the proposed framework integrates advanced machine learning with real-time industrial automation. The methodology involves the transformation of motor signals into time-frequency representations, followed by feature extraction and fault classification using ResNet-50. The trained model is optimized and deployed on a Siemens S7-315 PLC, enabling real-time diagnostics with minimal latency. Experimental results demonstrate classification accuracies exceeding 95% across various fault scenarios, with a detection latency of approximately 150 ms, significantly outperforming traditional methods. This study also identifies challenges such as computational constraints, noise sensitivity, and dataset limitations, offering solutions through adaptive algorithms, lightweight neural network architectures, and IoT-enabled edge computing. Future directions emphasize scalability, enhanced robustness, and integration with predictive maintenance systems. By combining cutting-edge machine learning techniques with industrial-grade hardware, this research provides a scalable, efficient, and reliable solution for real-time fault detection in induction motors, paving the way for intelligent and resilient industrial diagnostics.