Graduate Program in Instrumentation, Control and Automation of Mining Processes, Instituto Tecnológico Vale, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil.
VALE S.A., Parauapebas, Para 68516-000, Brazil.
Sensors (Basel). 2021 Dec 28;22(1):169. doi: 10.3390/s22010169.
There is a constant risk of iron ore collapsing during its transfer between processing stages in beneficiation plants. Existing instrumentation is not only expensive but also complex and challenging to maintain. In this research, we propose using edge artificial intelligence for early detection of landslide risk based on images of iron ore transported on conveyor belts. During this work, we defined the device edge and the deep neural network model. Then, we built a prototype will to collect images that will be used for training the model. This model will be compressed for use in the device edge. This same prototype will be used for field tests of the model under operational conditions. In building the prototype, a real-time clock was used to ensure the synchronization of image records with the plant's process information, ensuring the correct classification of images by the process specialist. The results obtained in the field tests of the prototype with an accuracy of 91% and a recall of 96% indicate the feasibility of using deep learning at the edge to detect the type of iron ore and prevent its risk of avalanche.
在选矿厂的选矿过程中，铁矿石在转运过程中存在着不断崩塌的风险。现有的仪器不仅昂贵，而且复杂，维护起来具有挑战性。在这项研究中，我们提出了使用边缘人工智能来对基于传送带上运输的铁矿石图像进行早期滑坡风险检测。在这项工作中，我们定义了设备边缘和深度神经网络模型。然后，我们构建了一个将用于收集图像的原型，这些图像将用于训练模型。这个模型将被压缩，用于设备边缘的使用。同一个原型将用于模型在操作条件下的现场测试。在构建原型时，使用实时时钟确保图像记录与工厂的过程信息同步，确保过程专家对图像进行正确分类。原型的现场测试结果准确率为 91%，召回率为 96%，这表明在边缘使用深度学习来检测铁矿石的类型并防止其崩塌的风险是可行的。
