IoT-Driven Predictive Maintenance for Energy-Efficient Industrial Systems

This research examines the deployment of the internet of things (IoT) technologies for predictive maintenance in industrial facilities in an effort to achieve energy efficiency and ensure the integration of renewable energy sources. Through the application of various IoT sensors and predictive algorithms at different industrial spaces, the study will show what the effect of predictive maintenance on operating costs, equipment life, and energy consumption. The results show that the reduction in downtime and energy expenditure, which, in turn, helps in the optimization of renewable energy usage in favor of environmental sustainability. The study brings to the foreground the practical obstacles, like the privacy of data and system integration problems, and puts forward the measures that would enhance their resolution. Such results support the potency of IoT focused preventive maintenance in constructing a ‘green’, smart and productive industrial sector.