烧结机台车缺陷部件在线智能检测系统设计与实现

Design and realization of online intelligent detection system for defective parts of sintering machine trolley

烧结机台车是制取烧结矿的关键设备,为避免由台车上的箅条缺失、车轮锁紧螺母缺失与车轮脱落引发的生产事故,提高烧结生产效率。本文根据台车实际运行状况,在硬件层面制定了故障检测方案,构建了基于YOLOv7与DeepStream的烧结机台车缺陷部件检测系统。系统选取YOLOv7网络模型在缺陷部件数据集上训练,将YOLOv7模型训练所得权重文件部署在DeepStream6.1平台进行加速推理,并采用Kafka消息组件推送推断结果。试验结果表明,YOLOv7对所有类别检测的平均准确率为0.991,可用于缺陷部件的目标检测。系统实时监测烧结机台车运行情况,对Kafka消息进行解析,实现故障判定规则,通过实时的故障判定、存储、显示及预警实现烧结机台车缺陷部件智能化检测,为烧结机台车缺陷部件检修提供了一种新的解决方案。

Sintering machine trolley is the key equipment for producing sintered ore,in order to avoid the production accidents caused by the missing grate bars,missing wheel lock nuts and wheel falling off on the trolley,and to improve the sintering production efficiency.According to the actual operating conditions of trolleys,a fault detection scheme was formulated at the hardware level,and a sintering machine trolley defective parts detection system based on You Only Look Once version 7(YOLOv7)and DeepStream was constructed.The YOLOv7 network model was selected to be trained on the defective parts dataset,and a weight file obtained from the training of the YOLOv7 model was deployed on DeepStream6.1 platform for accelerated inference,and Kafka messaging component was used to push the inference results.Experimental results show that the average precision of YOLOv7 for all classes of detection was 0.991,which can be used for the target detection of defective parts.System monitors the operation of sintering machine trolley in real time,and parses Kafka messages to realize fault determination rules.Through real-time fault determination,storage,display and alarm,the system realizes intelligent detection of defective parts of sintering machine trolley,and provides a new solution for maintenance of defective parts of sintering machine trolley.