Intelligent Perception and Recognition of Blast Furnace Anomalies via Burden Surface Video Image Analysis
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摘要: 智能感知、精准识别高炉(Blast furnace, BF)异常状态对高炉调控优化和稳定运行至关重要, 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, 并提取煤气流图像多元特征; 其次, 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, 结合料面视频图像, 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, 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.
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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
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 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 multi-features of gas flow images. Secondly, 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 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 proposed method can accurately recognize different BF anomalies with a fast recognition speed, providing crucial assurance for the smooth operation of the BF. -
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)
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
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1; (b) 稳定2; (c) 煤气流状态异常; (d) 悬料1; (e) 悬料2; (f) 高料位)
Fig. 3 Comparison results of high temperature gas flow images acquired by different methods under different BF conditions ((a) Stable 1; (b) Stable 2; (c) Abnormal gas flow status; (d) Hanging 1; (e) Hanging 2; (f) High stockline)
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
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 different BF statuses after dimensionality reduction ((a) Normal status; (b) Abnormal status)
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 different BF statuses after clustering ((a) Normal status; (b) Abnormal status)
图 11 基于HGJM趋势变化的煤气流异常状态感知结果
Fig. 11 The perception results of gas flow anomaly based on HGJM trend change
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)
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)↑ 塌料 煤气流异常 悬料 正常状态 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 注: ↑ 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 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% $\underline{17.00\%}$ SD-DAE 93.77% 10.40% 料面图像法 所提方法 99.58% 0.21% 注: ↑ 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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