自动化学报2024，Vol.50Issue(7)：1345-1362,18.DOI:10.16383/j.aas.c230674
基于料面视频图像分析的高炉异常状态智能感知与识别
Intelligent Perception and Recognition of Blast Furnace Anomalies via Burden Surface Video Image Analysis
摘要
Abstract
The intelligent perception and precise recognition of blast furnace(BF)anomalies are important for BF regulation,optimization and stable operation.However,the opaque nature of the internal workings of the BF makes it difficult for traditional detection methods to directly perceive and accurately recognize various BF anomalies.The novel industrial endoscope can capture a large number of BF burden surface video images,providing a new way for direct observation of the furnace's operational status.Based on this,an intelligent perception and precise recogni-tion method for BF anomalies is proposed via burden surface video image analysis.Firstly,a method for extracting high-temperature gas flow regions based on multi-scale texture fuzzy C-means(MST-FCM)clustering is proposed to accurately obtain gas flow images and extract multi-features of gas flow images.Secondly,a high-dimensional fea-ture dimensionality reduction method based on feature encoding is proposed,which is combined with the adaptive K-means++algorithm to achieve coarse-grained perception of gas flow anomalies.On this basis,a fine-grained per-ception method for gas flow anomalies is proposed by refining Jacobi-Fourier moments(JFM)to extract the deep feature change trend of gas flow images.Finally,based on the perception results of gas flow anomalies,and com-bined with BF video images,a multi-level residual channel attention module(MRCAM)is put forward and the BF anomalies recognition model ResVGGNet is established.This model achieves precise and online recognition of gas flow anomalies,collapsing and hanging burden surface in the BF.Experimental results demonstrate that the pro-posed method can accurately recognize different BF anomalies with a fast recognition speed,providing crucial assur-ance for the smooth operation of the BF.关键词
高炉/料面图像/高炉异常状态感知/高炉异常状态识别/多级残差通道注意力模块Key words
Blast furnace(BF)/burden surface image/BF anomalies perception/BF anomalies recognition/multi-level residual channel attention module(MRCAM)引用本文复制引用
朱霁霖,桂卫华,蒋朝辉,陈致蓬,方怡静..基于料面视频图像分析的高炉异常状态智能感知与识别[J].自动化学报,2024,50(7):1345-1362,18.基金项目
国家重大科研仪器研制项目(61927803),国家自然科学基金基础科学中心项目(61988101),国家自然科学基金(62273359),湘江实验室重大项目(22XJ01005)资助Supported by National Major Scientific Research Equipment of China(61927803),National Natural Science Foundation of China Basic Science Center Project(61988101),National Natural Science Foundation of China(62273359),and the Major Program of Xi-angjiang Laboratory(22XJ01005) （61927803）
