Predictive Maintenance Using Machine Learning

Predictive Maintenance Using Machine Learning is a transformative approach designed to address the inefficiencies of traditional industrial maintenance strategies. Unlike reactive maintenance (fixing after failure) or preventive maintenance (scheduled regardless of condition), this project leverages machine learning models to analyze historical and real-time data to identify potential equipment failures before they occur. The system employs algorithms such as Random Forest and Neural Networks to predict equipment conditions and generate real-time alerts. By integrating IoT sensor data (vibration, temperature, pressure), the application enables industries to reduce unplanned downtime, lower maintenance costs, and extend equipment lifespan. The project demonstrates a scalable solution applicable across industries, aligning with Industry 4.0 standards for operational efficiency and reliability.