Predictive Maintenance System

Maintenance of industrial systems is essential to ensure safe operation, reliability, and long equipment life. Traditional maintenance techniques such as reactive and preventive maintenance are inefficient for systems that require continuous monitoring and early fault detection [1][5]. Reactive maintenance leads to unexpected failures and increased downtime, while preventive maintenance often results in unnecessary servicing. This project presents a Predictive Maintenance System that continuously monitors critical parameters such as RPM, temperature, sound, voltage, and current to assess system health in real time [2][6]. A demo windmill model is used to demonstrate the working principle of the system. Sensor data is analyzed using predefined threshold values to identify abnormal operating conditions and predict possible faults before actual failure occurs. The system provides real-time data display, fault alerts, and maintenance suggestions through an LCD and a dashboard interface. By enabling early fault detection and guided maintenance actions, the proposed system reduces downtime, lowers maintenance costs, and improves overall system reliability, making it an effective and intelligent maintenance solution.