Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage

Featured Application This work can potentially be applied to industrial belt conveyors of any type. The tested system can be used for real-time monitoring in order to identify and prevent overloads, misalignments, growing damage to the belt in the early stages, and other trends that may cause failure.Abstract This paper is devoted to the real-time monitoring of close transportation devices, namely, belt conveyors. It presents a novel measurement system based on the linear strain gauges placed on the tail pulley surface. These gauges enable the monitoring and continuous collection and processing of data related to the process. An initial assessment of the machine learning application to the load identification was made. Among the tested algorithms that utilized machine learning, some exhibited a classification accuracy as high as 100% when identifying the load placed on the moving belt. Similarly, identification of the preset damage was possible using machine learning algorithms, demonstrating the feasibility of the system for fault diagnosis and predictive maintenance.