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Abstract
This paper presents an innovative approach to motor bearing fault detection using TinyML on an IoT device. We developed a system that integrates spectral analysis and deep learning on a resource-constrained edge device, enabling real-time monitoring and anomaly detection. Our method achieves 96.5(% accuracy in laboratory outperforming baseline Random Forest and SVM models. The system's low latency (300 ms from data collection to alert generation) and computational efficiency make it suitable for real-time industrial applications. We address challenges such as environmental noise and connectivity issues and discuss future directions including multi-modal sensor integration and federated learning. This research contributes to the growing field of edge AI for predictive maintenance, demonstrating the viability of sophisticated machine learning models on low-power microcontrollers.