Towards to an Embedded Edge AI Implementation for Longitudinal Rip Detection in Conveyor Belt - Klippel, Emerson; Oliveira, Ricardo Augusto; Maslov, Dmitry; Bianchi, Andrea; Silva, Saul Emanuel; Garrocho, Charles Tim (Anais Estendidos do Simpósio Brasileiro de Engenharia de Sistemas Computacionais (SBESC), 2020)
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 resulting model is converted and executed in an edge device located near the conveyor belt to stop it in case a failure is detected. The results obtained in the development and tests carried out to date show the feasibility of using Edge AI to solve complex problems in a mining environment such as detecting longitudinal rips and stimulate the continuity of the work considering new scenarios and operational conditions in the search for a robust and replicable solution.