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基于深度卷积网络的电熔镁炉欠烧工况在线识别

Online detection of semi-molten of fused magnesium furnace based on deep convolutional neural network
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摘要 欠烧是电熔镁炉熔炼过程中由于原料杂质不均匀导致炉壁局部过热的异常工况,若不及时发现和处理,可能导致炉体烧穿。目前,欠烧工况主要依靠有经验的巡检工人在电熔镁生产现场“看火”,劳动强度大且危险性高,容易漏检、误检。鉴于此,提出一种基于深度卷积网络的可见光RGB图像与红外热像相结合的电熔镁炉欠烧工况感知技术,并基于此开发原型系统。采用工业相机和红外热像仪获取电熔镁生产现场过程图像,利用深度学习技术并结合现场工人经验建立对欠烧工况视频图像的检测和识别模型,通过实时的图像分析,实现对欠烧工况的在线识别。将该技术在某氧化镁企业进行工业实验,验证了所提出技术的有效性。 In the smelting process of fused magnesium furnace, semi-molten is the abnormal working condition that burns the furnace wall to red because of the uneven impurities in material. If it is not detected and dealt with timely,the furnace can be burnt through. At present, the detection of semi-molten mainly relies on experienced operators by"observing fire"at the scene of the fused magnesium production. The environment of scene is hostile and the working intensity is high. The human observation may cause safety issues and can lead to overlook and mistakes. This work introduces a detection technology for the semi-molten working conditions of fused magnesium furnace based on the deep convolutional neural network(CNN) model trained using historical images of visible and infrared thermal sensors.A prototype system is developed based on this technology. An industrial camera and an infrared thermal imager are wed to acquire images of the fused magnesium productive process, and the deep learning technology is combined with the working condition of workers’ experience to build the detection and recognition model. With the system, on-line identification of semi-molten condition through real-time image analysis is achieved. The proposed technology is tested in a factory of electric-fused magnesium, which can demonstrate its effectueners.
作者 卢绍文 王克栋 吴志伟 李鹏琦 郭章 LU Shao-wen;WANG Ke-dong;WU Zhi-wei;LI Peng-qi;GUO Zhang(State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004,China)
出处 《控制与决策》 EI CSCD 北大核心 2019年第7期1537-1544,共8页 Control and Decision
基金 国家自然科学基金项目(61473071).
关键词 电熔镁炉 深度学习 工况感知 图像检测 识别模型 生成对抗网络 fused magnesium furnace deep learning working condition detection image detection recognition model generative adversarial networks
作者简介 通讯作者:卢绍文,E-mail:lusw@mail.neu.edu.cn.
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