Authors - Elakkiya R, Sagunthala G, Tanisha Sinha, Gugapriya G Abstract - This project presents an IoT-based predictive maintenance system based on machine learning algorithms—Random Forest, Logistic Regression, SVM, and LSTM—to identify motor faults precisely. Realtime data such as sound, vibration, and RPM are recorded through hardware prototyping, while Simulink simulates speed and torque. Data is transmitted to Firebase for real-time monitoring, prompting automated fault notifications. This system improves industrial efficiency by minimizing sudden failures, reducing maintenance expenses, and increasing machinery lifespan through prompt, data-driven interventions.
