Real-Time IoT-Based Predictive Maintenance System for Automotive Assembly Lines

Abstract

The real-time use of IoT-based predictive maintenance systems integrated into automotive assembly lines is a revolutionary measure to increase operational effectiveness, reduce downtime, and prolong the useful life of key equipment. This mechanism utilizes a set of IoT-enabled sensors to continuously monitor device-specific parameters, including temperature, vibration, pressure, and acoustic values. The data gathered is relayed to cloud or edge computing servers, where it is analyzed by machine learning algorithms to establish trends, identify anomalies, and predict imminent failures before they occur. This predictive maintenance replaces traditional reactive, scheduled maintenance, whereby planned maintenance interventions are provided before problems occur, thereby minimizing unplanned machine downtimes. Maintenance activities are optimized according to the real state of machines. The system helps enhance line productivity, reduce maintenance costs, and improve resource utilization. It also increases quality and safety by eliminating errors that may occur in the equipment and thus affect the accuracy of vehicle assembly. Such systems are deployed in relation to Industry 4.0 priorities, enabling more informed decisions with data and contributing to more flexible and adaptable production processes. Automotive manufacturers will not only find the benefits of this method in increased efficiency but also in a competitive advantage on the market, regarding the quality of the produced units and the decrease in risks and costs associated with operational procedures.