基于SSD算法的矿用电铲铲斗健康监测方法

Health monitoring method of mining electric shovel bucket based on SSD algorithm

矿用电铲是露天矿生产作业过程中的重要采掘装备,铲斗上各部件脱落在电铲工作过程中时有发生,且不易被及时发现。这些部件如果随着矿石进入下游破碎环节,很容易引起破碎机损坏,造成生产线停机维修,导致巨大的经济损失。目前,对于铲斗部件脱落的监视主要还是依靠电铲司机目视判断,这种方式效率低、准确性差、分散司机的精力。基于深度学习和机器视觉技术,研究了一种电铲铲斗健康监测方法,以快速准确地完成铲斗部件脱落监测,并设计了相应的监测系统。在SSD预处理模型的基础上对模型进行了优化和训练,在电铲铲斗大臂上安装工业相机,采集图像并输入驾驶室一体机,一体机调用训练模型,结合机器视觉技术监测铲斗部件是否脱落。在鞍钢矿业齐大山铁矿对监测方法进行了为期半年的准确性验证,准确率达到90%以上。研究结果表明,该方法能够实时监测电铲铲斗部件脱落,解决了传统方法只能靠人眼识别的问题,减轻了劳动强度,减少了非必要停工,保证连续生产。

Mining electric shovel is an important equipment of open pit mine production,parts on bucket fall off frequently during the work of shovel,and it is not easy to be found in time.If these parts enter the downstream crushing link along with the ore,it is easy to cause damage to crusher,resulting in shutdown of the production line for maintenance and huge economic losses.At present,the monitoring of bucket parts falling off mainly depends on the visual judgment of the shovel driver,it is inefficient and inaccurate,which distracts the driver.Based on deep learning and machine vision technology,a method for monitoring is studied to complete the detection of the bucket parts falling off,and a corresponding detection system is designed.Based on SSD preprocessing model,the model is optimized and trained.An industrial camera is installed on the bucket boom of the electric shovel,images are collected and input into the touch control machine in the cab to call the training model,and machine vision technology is used to detect whether the bucket parts fell off.In Qidashan Iron Mine of Ansteel Mining,the accuracy of the detection method is verified for half a year,and the accuracy rate reached 90%.The results show that the method can detect the parts falling off in real time,solve the problem that the traditional method can only be identified by human eyes,reduce labor intensity,reduce unnecessary downtime,and ensure continuous production.