Predictive Maintenance for Industrial IOT using Deep Learning

In modern industrial settings, predictive maintenance has become indispensable for reducing downtime and enhancing operational efficiency. Traditional maintenance strategies, based on fixed schedules or reactive measures, often overlook the potential of real-time sensor data generated by Industrial IoT systems. Predictive Maintenance project focuses on integrating advanced forecasting techniques, such as ARIMA models, and machine learning classification algorithms with IoT-enabled sensor networks to develop an effective predictive maintenance system. The objective is to develop an efficient and high-performing system that can analyze real-time data, anticipate equipment failures, and streamline maintenance schedules while keeping costs minimal. The proposed solution emphasizes building a resilient framework for data acquisition, preprocessing, model training, and real-time monitoring. This enables industries to minimize unplanned downtime, reduce maintenance expenses, and improve operational performance, aligning with the transformative goals of Industry 4.0.