AI Based Condition Monitoring Of Conveyor Belt Systems Using Machine Learning Deep Learning And IoT

Abstract

The significance of predictive monitoring for industrial equipment as a vital newlinefacilitator of efficiency, quality, and safety is being emphasized more and more by newlineindustrial research and contemporary researchers. These systems provide early fault newlinedetection through the use of IoT, machine learning, and deep learning technologies, newlineenabling prompt intervention before problems worsen and result in expensive failures. newlineThis proactive strategy improves operator safety, reduces downtime, and maintains newlineconsistent production quality. Furthermore, it prolongs the life of equipment and lowers newlinemaintenance expenses, promoting dependable, high-performing, and sustainable newlineindustrial operations across industries. newlineConveyor belts are essential components of mining, power, cement, and newlinemanufacturing facilities, where their continuous operation has a direct impact on output newlineand profitability. This is one of the industrial automation systems. Unplanned newlinebreakdowns result in safety risks, productivity losses, and expensive downtime. The newlinereactive and time-based nature of traditional maintenance techniques frequently makes newlineit impossible to anticipate failures in advance, which can result in catastrophic newlinebreakdowns or the premature replacement of parts. By continuously evaluating belt newlinehealth, identifying anomalies early, and enabling predictive maintenance, condition newlinemonitoring made possible by AI, IoT, and sophisticated analytics fills this gap. newlineIn line with Industry 4.0 goals, this guarantees improved safety standards, cost newlinesavings, and operational dependability. The combination of real-time visualization, newlinecloud-based analytics, and multi-sensor data collection has become a game-changing newlinestrategy in recent years. Nevertheless, current systems frequently depend on discrete newlinealgorithms or a small number of sensor inputs, which limits fault coverage. This study newlinecreates a multi-sensor, artificial intelligence (AI)-powered condition monitoring system newlinethat can identify various mechanical, thermal