👉 This project involves developing an AI-driven predictive maintenance system for industrial machinery, leveraging machine learning to forecast equipment failures before they occur. The system integrates real-time sensor data from machinery with historical maintenance records and environmental conditions to train a predictive model. Using techniques like anomaly detection and time-series forecasting, the model identifies patterns indicative of potential failures. Once a failure is predicted, the system generates alerts and recommendations for maintenance teams, optimizing downtime and reducing repair costs. The project includes data preprocessing, model selection and training, deployment of the predictive engine, and a user interface for maintenance personnel to interact with the system, ensuring proactive and efficient maintenance strategies.
