Enhancing Industrial Reliability Through Predictive Maintenance Using AI and Sensor Analytics

An extensive overview of a predictive maintenance (PdM) system designed for industrial applicationsis provided. With predictive maintenance, equipment faults are predicted before they happen by using data-driven methods like machine learning and sensor data analysis. The suggested PdM system attempts to lower unplanned downtime, optimize maintenance schedules, and improve overall operational efficiency by com- bining real-time monitoring and historical data analysis. This system will include case studies illustrating the efficacy of PdM in several industrial contexts, as well as a description of the methodology for feature extraction, data collecting, and model construction. Significant cost savings, increased asset reliability, and a decrease in interruptionsfrom maintenance are among the anticipated results. In the end,this system highlights the significance of implementing a predictive maintenance strategy in contemporary industrialsettings, opening the door for a more sustainable and efficient future.