Towards to an Embedded Edge AI Implementation for Longitudinal Rip Detection in Conveyor Belt
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.
A. Krizhesky, I. Sutskever, G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, Neural Information Processing System Conference, 2012.
Raghu, Maithra, and Eric Schmidt. "A survey of deep learning for scientific discovery." arXiv preprint arXiv:2003.11755 (2020).
A. Malik, A. Gupta, Artificial Intelligence at the Edge. 1st ed.,Amazon Publishing, United States of America, 2020.
L. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, & A. Y. Zomaya,”Edge intelligence: the confluence of edge computing and artificial intelligence”, IEEE Internet of Things Journal, 2020.
[NI], “Google Coral”. https://coral.ai/software/. 2020.
Sunnycase, “Kendrite nncase”. https://github.com/kendryte/nncase.2020.
[NI], “OpenVino Intel”. https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html. 2020.
L. Deng, G. Li, S. Han, L. Shi and Y. Xie, “Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey”, IEEE, vol 108, No. 4, 2020.
X. Li, L. Shen, Z. Ming, C. Zhang and H. Jiang, “Laser-based on-line machine vision detection for longitudinal rip of conveyor belt”, Optik, v. 168, pp. 360-369, 2018.
H. Chengchegn, Q. Tiezhu, Q. Meiying. X. Xiaoyan, Y. Yi and Z.Haitao. “Researhc on audio-visual detection method for conveyor belt longitudinal tear”, IEEE Access, vol VII, pp. 120202-120213, 2019.
A. A. Santos, F. A. S. Rocha, H. Azpúrua, A. J. R. Reis and F. G. Guimarães, “Automatic system for visual inspection of belt conveyors”, SBA, 14˚ Intelligent Automation Symposium, pp. 1192-1197, 2019.
Majidifard, Hamed, et al. "Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses." Transportation Research Record 2674.2 (2020): 328-339.
D. Maslov “Image Recognition With K210 Boards and Arduino IDE/Micropython”. https://www.instructables.com/id/Transfer-Learning-With-Sipeed-MaiX-and-Arduino-IDE. 2020.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, & H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications”, arXiv preprintarXiv:1704.04861. 2017.