INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD

Abstract : Predictive maintenance has emerged as a crucial approach to optimize industrial equipment’s performance, minimize downtime, and reduce maintenance costs. This research paper presents the design and implementation of a predictive maintenance system for industrial equipment leveraging the combined power of Random Forest Algorithm and  Decision  Tree Algorithms with Internet of Things (IoT). The Proposed system utilizes IoT sensors to collect real-time data from the equipment, including temperature, vibration, pressure, and other critical parameters. For industries aiming to maximize machinery efficiency and reduce unscheduled downtime, predictive mainte- nance has become more and more important. The power of the Random Forest and Decision Tree algorithms combined with IoT technology is harnessed in this research study to propose a unique predictive maintenance system for industrial equipment. IoT sensors are strategically positioned on industrial machines as part of the system to gather real-time data on important operating characteristics. In order to identify anomalies, forecast potential failures, and estimate the equipment’s remaining usable life, the data is then processed and analyzed using the Random Forest and Decision Tree algorithms. The system’s predictive abilities allow for preventive maintenance procedures, cutting downtime and operational expenses while enhancing the performance and durability of the equipment.

Index Terms—Predictive Maintenance (PdM), Industrial Equipmen, IoT (Internet of Things), Machine Learning, Sensors, Anomaly Detection, Real time Monitoring Predictive Models, Condition Monitoring Random Forest Algorithm, Decision Tree Algorithms Introduction: