Embedded Edge Artificial Intelligence for Longitudinal Rip Detection in Conveyor Belt Applied at the Industrial Mining Environment

The use of deep learning on edge AI to detect failures in conveyor belts solves a complex problem of iron ore beneficiation plants. Losses in the order of thousands of dollars are caused by failures in these assets. The existing fault detection systems currently do not have the necessary efficiency and complete loss of belts is common. Correct fault detection is necessary to reduce financial losses and unnecessary risk exposure by maintenance personnel. This problem is addressed by the present work with the training of a deep learning model for detecting images of failures of the conveyor belt. The resulted model is converted and executed in an edge device located near the conveyor belt to stop it in case a failure is detected. A prototype built and tested in the field obtained satisfactory results and is shown as the feasibility of using deep learning and edge artificial intelligence in industrial mining environments.