Health monitoring of a conveyor belt system using machine vision and real-time sensor data

Abstract

To truly achieve prognostic health monitoring of complex manufacturing systems, real-time data from multiple sensors must be continuously monitored in order to have a system-wide view of the health of the machine and the effect of different operating conditions on system performance. Belt misalignment is a critical factor in the conveyor belt system traditionally assessed through mechanical means. This research successfully implements multiple sensors and communication protocols along with machine vision for monitoring the health of a conveyor belt system. Data is captured using industrial-grade sensors and conditioned in a PLC. This information is sent to a wireless radio frequency module that transmits wirelessly to a remote receiver paired to an IoT gateway. Images taken by a camera are simultaneously processed by a local computer running a machine vision algorithm used to establish if the conveyor belt is operating normally. Both the sensor data and the machine vision information are sent to the cloud for remote users to monitor the system's operating conditions and to detect potential failure prior to its occurrence. The system communicates using a set of Radio Frequency modules, Zumlink™, and the range of this system was tested and determined to be greater than 2.5 miles with obstructions typical in a manufacturing environment.