IoT device for detecting abnormal vibrations in motors using TinyML
Stalin Arciniegas, Dulce Rivero, Jefferson Piñan, Elizabeth Diaz, Francklin Rivas
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.