基于料面视频图像分析的高炉异常状态智能感知与识别

朱霁霖; 桂卫华; 蒋朝辉; 陈致蓬; 方怡静

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

doi: 10.16383/j.aas.c230674

朱霁霖^1,,
桂卫华^{1, 2,},
蒋朝辉^{1, 2,},
陈致蓬^1,,
方怡静^1,

1.
中南大学自动化学院长沙 410083
2.
湘江实验室长沙 410205

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

详细信息

作者简介:
朱霁霖：中南大学自动化学院博士研究生. 2017年获得南京理工大学学士学位, 2020年获得中南大学硕士学位. 主要研究方向为先进检测技术, 图像处理和工业过程建模与监测. E-mail: zhujilin@csu.edu.cn

桂卫华：中国工程院院士, 中南大学自动化学院教授. 1981年获得中南矿冶学院硕士学位. 主要研究方向为复杂工业过程建模, 优化与控制应用和故障诊断与分布式鲁棒控制. E-mail: gwh@csu.edu.cn

蒋朝辉：中南大学自动化学院教授. 2011年获中南大学博士学位. 主要研究方向为智能传感与检测技术, 图像处理与智能识别和人工智能与机器学习. 本文通信作者. E-mail: jzh0903@csu.edu.cn

陈致蓬：中南大学自动化学院副教授. 2018年获中南大学博士学位. 主要研究方向为图像处理, 仪器检测, 冶金过程建模与控制. E-mail: ZP.Chen@csu.edu.cn

方怡静：中南大学自动化学院博士研究生. 2016年和2019年分别获得中南大学学士学位和硕士学位. 主要研究方向为数据驱动的工业过程建模与控制, 工业过程数据分析和机器学习. E-mail: yijingfang@csu.edu.cn

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出版历程
- 收稿日期: 2023-11-01
- 录用日期: 2024-02-29
- 网络出版日期: 2024-06-03

Intelligent Perception and Recognition of Blast Furnace Anomalies via Burden Surface Video Image Analysis

ZHU Ji-Lin^1
,,
GUI Wei-Hua^{1, 2
,},
JIANG Zhao-Hui^{1, 2
,},
CHEN Zhi-Peng^1
,,
FANG Yi-Jing^1
,

1.
School of Automation, Central South University, Changsha 410083
2.
Xiangjiang Laboratory, Changsha 410205

Funds: 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 Xiangjiang Laboratory (22XJ01005)

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Author Bio:
ZHU Ji-Lin　Ph.D. candidate at the School of Automation, Central South University. He received his bachelor degree from Nanjing University of Science and Technology in 2017 and master degree from Central South University in 2020. His research interest covers advanced detection technology, image processing, and industrial process modeling and monitoring

GUI Wei-Hua　Academician of Chinese Academy of Engineering, and professor at the School of Automation, Central South University. He received his master degree from Central South Institute of Mining and Metallurgy in 1981. His research interest covers complex industrial process modeling, optimization and control applications, and fault diagnosis and distributed robust control

JIANG Zhao-Hui　Professor at the School of Automation, Central South University. He received his Ph.D. degree from Central South University in 2011. His research interest covers intelligent sensing and detection technology, image processing and intelligent recognition, and artificial intelligence and machine learning. Corresponding author of this paper

CHEN Zhi-Peng　Associate professor at the School of Automation, Central South University. He received his Ph.D. degree from Central South University in 2018. His research interest covers image processing, instrument detection, and modeling and control of metallurgical process

FANG Yi-Jing　Ph.D. candidate at the School of Automation, Central South University. She received her bachelor degree and master degree from Central South University in 2016 and 2019, respectively. Her research interest covers data-based modeling and control of industrial process, data analysis of industrial process, and machine learning

摘要

摘要: 智能感知、精准识别高炉(Blast furnace, BF)异常状态对指导高炉调控优化、保证高炉稳定运行具有重要意义, 但高炉内部的黑箱状态致使传统检测方法难以直接感知并准确识别多种高炉异常状态. 新型工业内窥镜可获取大量料面视频图像, 为直接观测炉内运行状态提供了全新的手段. 基于此, 提出一种基于料面视频图像分析的高炉异常状态智能感知与识别方法. 首先, 提出基于多尺度纹理模糊C均值(Multi-scale texture fuzzy C-means, MST-FCM)聚类的高温煤气流区域提取方法, 准确获取煤气流图像, 并提取煤气流图像纹理、形态和稳定性等浅层特征; 其次, 针对高炉煤气流异常状态感知缺乏相应手段的问题, 提出基于特征编码的高维特征降维方法, 结合自适应K-means++算法, 实现煤气流异常状态的粗粒度感知; 在此基础上, 通过改进雅可比−傅立叶矩(Jacobi-Fourier moments, JFM) 提取煤气流图像深层特征变化趋势, 进而提出细粒度煤气流异常状态感知方法; 最后, 基于煤气流异常状态感知结果, 结合塌料和悬料视频图像, 提出多级残差通道注意力模块(Multi-level residual channel attention module, MRCAM), 建立高炉异常状态识别模型ResVGGNet, 同时实现高炉煤气流异常、塌料和悬料的精准在线识别. 实验结果表明, 所提方法能准确识别不同的高炉异常状态且识别速度快, 可为高炉平稳运行提供重要保障.
- 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
Abstract: The intelligent perception and precise recognition of blast furnace (BF) anomalies hold significant importance for guidance in BF regulation and optimization and ensuring stable BF 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 recognition 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 shallow features such as texture, morphology and stability of gas flow images. Secondly, addressing the lack of corresponding means for perceiving gas flow anomalies in BF, a high-dimensional feature 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 perception 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 combined with video images of collapsing and hanging burden surface, 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 proposed method can accurately recognize different BF anomalies with a fast recognition speed, providing crucial assurance for the smooth operation of the BF.
- Blast furnace (BF) /
- burden surface image /
- BF anomalies perception /
- BF anomalies recognition /
- multi-level residual channel attention module (MRCAM)

HTML全文

图 1 新型工业内窥镜安装示意图((a) 新型工业内窥镜实际安装位置; (b) 新型工业内窥镜成像区域示意图)

Fig. 1 The schematic diagram of installation of the novel industrial endoscope ((a) Actual installation position of the novel industrial endoscope; (b) Schematic diagram of imaging area of the novel industrial endoscope)

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图 2 不同高炉运行状态下的高炉料面图像

Fig. 2 BF burden surface images under different BF operation statuses

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图 3 不同炉况下不同方法获取高温煤气流图像对比结果((a)稳定; (b) 稳定; (c) 煤气流状态异常; (d) 悬料; (e) 悬料; (f) 高料位)

Fig. 3 Comparison results of high temperature gas flow images acquired by different methods under different BF conditions ((a) Stable; (b) Stable; (c) Abnormal gas flow status; (d) Hanging; (e) Hanging; (f) High stockline)

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图 4 基于料面视频图像分析的高炉异常状态智能感知与识别框图

Fig. 4 Block diagram of intelligent BF anomalies perception and recognition via burden surface video image analysis

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图 5 雅可比–傅立叶矩积分区域

Fig. 5 The integral region of Jacobian-Fourier moment

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图 6 ResVGGNet模型结构

Fig. 6 The structure of ResVGGNet model

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图 7 残差结构

Fig. 7 The residual structure

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图 8 RCAM结构

Fig. 8 The structure of RCAM

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图 9 不同状态下高温煤气流图像多元特征降维结果((a) 正常状态; (b) 异常状态)

Fig. 9 The multi-feature results of high-temperature gas flow images from normal and abnormal BF statuses after dimensionality reduction ((a) Normal status; (b) Abnormal status)

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图 10 不同状态下高温煤气流图像多元特征聚类结果((a) 正常状态; (b) 异常状态)

Fig. 10 The multi-feature results of high-temperature gas flow images from normal and abnormal BF statuses after clustering ((a) Normal status; (b) Abnormal status)

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图 11 基于HGJM趋势变化的煤气流异常状态感知结果

Fig. 11 The anomaly perception results based on HGJM trend change

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图 12 不同分类网络模型训练与测试结果((a) 训练精度; (b) 训练损失; (c) 测试精度; (d) 测试损失)

Fig. 12 Training and test results of different classification network models ((a) Training accuracy; (b) Training loss; (c) Test accuracy; (d) Test loss)

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表 1 高炉料面图像数据集

Table 1 Dataset of BF burden surface images

高炉状态	塌料	煤气流异常	悬料	正常
训练集	640	1920	960	1920
测试集	160	480	240	480

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表 2 不同分类网络在高炉料面图像数据集下的识别结果

Table 2 Recognition results of different classification networks under BF burden surface image dataset

网络名称	异常状态检测率↑			误报率↓	速度(帧/s)↑
网络名称	塌料	煤气流异常	悬料	正常状态	速度(帧/s)↑
ResNet18	100.00%	98.54%	99.58%	0.42%	42.94
VGG11	100.00%	98.33%	99.58%	2.29%	35.29
ViT16	100.00%	99.38%	$\underline{98.75\%}$	1.67%	23.32
SwinT-t	100.00%	$\underline{93.96\%}$	99.58%	$\underline{3.96\%}$	$\underline{8.98}$
ResVGGNet	100.00%	99.30%	99.58%	0.21%	60.26
↑ 表示指标越大越好, ↓ 表示指标越小越好, 粗体表示指标最优, 下划线表示指标最差.

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表 3 不同高炉异常状态识别方法对比

Table 3 Comparison among different BF anomaly recognition methods

类型	方法名称	悬料状态	正常状态
类型	方法名称	检测率↑	误报率↓
多元统计分析	CA	$\underline{71.20\%}$	4.60%
	MWPCA	96.45%	3.76%
	SFICVA	89.50%	3.00%
	L-DBKSSA	100.00%	1.24%
	A-DiASSA	92.80%	1.40%
深度学习	DSKL-SVM	100.00%	$\underline{17.00\%}$
深度学习	SD-DAE	93.77%	10.40%
料面图像法	所提方法	99.58%	0.21%
↑ 表示指标越大越好, ↓ 表示指标越小越好, 粗体表示指标最优, 斜体表示指标第二优, 下划线表示指标最差.

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