👉 The seventh project detailed here involves the development of an advanced AI-driven predictive maintenance system for industrial machinery. This system utilizes machine learning algorithms to analyze real-time sensor data from equipment, predicting potential failures before they occur. By continuously monitoring operational parameters such as temperature, vibration, and pressure, the AI model learns patterns indicative of wear and tear, enabling proactive maintenance scheduling. This approach minimizes downtime, reduces repair costs, and extends the lifespan of machinery. The project integrates IoT devices for data collection, a cloud platform for data processing and model training, and a user-friendly dashboard for maintenance teams to monitor predictions and take action.
