基于光流法和深度学习的燃气火焰稳定性

doi:10.16183/j.cnki.jsjtu.2020.111

上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (4): 462-470.doi: 10.16183/j.cnki.jsjtu.2020.111

所属专题：《上海交通大学学报》2021年12期专题汇总专辑；《上海交通大学学报》2021年“能源与动力工程”专题

基于光流法和深度学习的燃气火焰稳定性

王宇, 余岳峰(), 朱小磊, 张忠孝

上海交通大学机械与动力工程学院, 上海 200240

收稿日期:2020-04-15 出版日期:2021-04-28 发布日期:2021-04-30
通讯作者: 余岳峰 E-mail:yfyu@sjtu.edu.cn
作者简介:王宇(1995-),男,江苏省泰兴市人,硕士生,研究方向为图像火焰检测、模式识别及燃烧诊断
基金资助:
国家重点研发计划项目(2017YFF0209801)

Gas-Fired Flame Stability Based on Optical Flow Method and Deep Learning

WANG Yu, YU Yuefeng(), ZHU Xiaolei, ZHANG Zhongxiao

School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2020-04-15 Online:2021-04-28 Published:2021-04-30
Contact: YU Yuefeng E-mail:yfyu@sjtu.edu.cn

摘要/Abstract

摘要：

结合光流法和深度学习对燃气火焰稳定性进行了研究.采用光流法直接计算出火焰图像的光流矢量,观察火焰在二维图像中的脉动情况,并提出光流脉动评价模型,可以评估火焰的燃烧稳定性.此外,搭建基于VGG-Nets的深度卷积神经网络模型,在ImageNet预训练权重上进行微调,结合火焰静态与动态特征,实现了对五种典型燃烧状态的分类与识别.结果表明:该方法对火焰的不同燃烧状态具有很好的判断能力,对不稳定燃烧的火焰识别率很高.

关键词: 燃气火焰, 稳定性, 光流法, 深度学习

Abstract:

The stability of gas-fired flame is studied by combining the optical flow method and deep learning. The optical flow vector of the flame image is directly calculated by using the optical flow method. The pulsation of the flame in the two-dimensional image is observed, and an optical flow pulsation evaluation model is proposed to evaluate the stability of the flame. In addition, a deep convolutional neural network based on VGG-Nets is built and fine adjustments are made on ImageNet pre-training weights. Combining the static and dynamic characteristics of flames, the classification and recognition of five typical combustion states are achieved. The results show that this method has a good judgment ability for different combustion states of flames and a high recognition rate for unstable combustion flames.

Key words: gas-fired flame, stability, optical flow method, deep learning

中图分类号:

TK39

王宇, 余岳峰, 朱小磊, 张忠孝. 基于光流法和深度学习的燃气火焰稳定性[J]. 上海交通大学学报, 2021, 55(4): 462-470.

WANG Yu, YU Yuefeng, ZHU Xiaolei, ZHANG Zhongxiao. Gas-Fired Flame Stability Based on Optical Flow Method and Deep Learning[J]. Journal of Shanghai Jiao Tong University, 2021, 55(4): 462-470.

图/表 8

图1

表1

图2

图3

图4

图5

表2

图6

参考文献 16

[1]	娄春. 工程燃烧诊断学[M]. 北京: 中国电力出版社, 2016.
	LOU Chun. Engineering combustion diagnostics[M]. Beijing: China Electric Power Press, 2016.
[2]	白卫东, 严建华, 池涌, 等. PCA和SVM在火焰监测中的应用研究[J]. 中国电机工程学报, 2004, 24(2):186-191.
	BAI Weidong, YAN Jianhua, CHI Yong, et al. Application of PCA and SVM in flame monitoring[J]. Proceedings of the CSEE, 2004, 24(2):186-191.
[3]	吴一全, 宋昱, 周怀春. 基于灰度熵多阈值分割和SVM的火焰图像状态识别[J]. 中国电机工程学报, 2013, 33(20):66-73.
	WU Yiquan, SONG Yu, ZHOU Huaichun. Flame image state recognition based on gray entropy multiple threshold segmentation and SVM[J]. Proceedings of the CSEE, 2013, 33(20):66-73.
[4]	郭建民. 模糊免疫网络算法在数字图像火焰监测中的应用[J]. 中国电机工程学报, 2007, 27(2):2-5.
	GUO Jianmin. Application of fuzzy immune network algorithm in digital image flame monitoring[J]. Proceedings of the CSEE, 2007, 27(2):2-5.
[5]	SUN D, LU G, ZHOU H, et al. Condition monitoring of combustion processes through flame imaging and kernel principal component analysis[J]. Combustion Science and Technology, 2013, 185(9):1400-1413. doi: 10.1080/00102202.2013.798316 URL
[6]	GIRSHICK R. Fast R-CNN[C]//International Conference on Computer Vision. Santiago, USA: IEEE, 2015: 1440-1448.
[7]	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6):1137-1149.
[8]	韩溟. 基于光谱和图像综合测试系统的火焰特性研究[D]. 西安: 西安电子科技大学, 2007.
	HAN Ming. Study on flame characteristics based on spectrum and image synthesis test system[D]. Xi’an: Xidian University, 2007.
[9]	田正林, 余岳峰, 朱小磊, 等. 基于图像处理的燃气火焰稳定性检测试验研究[J]. 动力工程学报, 2019, 39(10):811-817.
	TIAN Zhenglin, YU Yuefeng, ZHU Xiaolei, et al. Experimental study on flame stability detection based on image processing[J]. Chinese Journal of Power Engineering, 2019, 39(10):811-817.
[10]	赵健, 王宾, 马苗. 数字信号处理[M], 北京: 清华大学出版社, 2012.
	ZHAO Jian, WANG Bin, MA Miao. Digital signal processing[M]. Beijing: Tsinghua University Press, 2012.
[11]	周怀春. 炉内火焰可视化检测原理与技术[M]. 北京: 科学出版社, 2005.
	ZHOU Huaichun. Principle and technology of visual flame detection in furnace[M]. Beijing: Science Press, 2005.
[12]	SUN D, ROTH S, BLACK M J. Secrets of optical flow estimation and their principles[C]//The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE, 2010: 2432-2439.
[13]	SUN D, ROTH S, BLACK M J, et al. A Quantitative analysis of current practices in optical flow estimation and the principles behind them[J]. International Journal of Computer Vision, 2014, 106(2):115-137. doi: 10.1007/s11263-013-0644-x URL
[14]	MUELLER M, KARASEV P, KOLESOV I, et al. Optical flow estimation for flame detection in videos[J]. IEEE Transactions on Image Processing, 2013, 22(7):2786-2797. doi: 10.1109/TIP.2013.2258353 URL
[15]	KOLESOV I, KARASEV P, TANNENBAUM A, et al. Fire and smoke detection in video with optimal mass transport based optical flow and neural networks[C]//Proceedings of the International Conference on Image Processing. Hong Kong, China: IEEE, 2010.
[16]	SIMONYAN, KAREN, ZISSERMAN, et al. Very deep convolutional networks for large-scale image recognition[C]//Ninth International Conference on Learning Representations. San Francisco, CA, USA: 2015: 1-14.

块	操作类型	输入维度	输出维度
B1	卷积×2,ReLU×2,平均汇合	224×224×3	112×112×64
B2	卷积×2,ReLU×2,平均汇合	112×112×64	56×56×128
B3	卷积×3,ReLU×3,平均汇合	56×56×128	28×28×256
B4	卷积×3,ReLU×3,平均汇合	28×28×256	14×14×512
B5	卷积×3,ReLU×3,平均汇合	14×14×512	7×7×512

火焰类型	训练组数	验证组数	平均正确率/%
扩散稳定	3881	971	100
扩散不稳定	197	50	99.95
预混稳定	4646	1162	100
预混不稳定	178	45	99.99
熄火及背景	244	61	100

基于光流法和深度学习的燃气火焰稳定性

Gas-Fired Flame Stability Based on Optical Flow Method and Deep Learning

RichHTML

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 16

相关文章 15

编辑推荐

Metrics

本文评价

[1]	. 基于改进YOLOv5l的交通信号灯识别[J]. J Shanghai Jiaotong Univ Sci, 2026, 31(2): 319-333.
[2]	郭琦, 闫军, 郝乾鹏, 韩东, 杨志豪, 闫馨月, 张海鹏, 李然. 基于闭环聚类和多目标优化的风电短期功率预测方法[J]. 上海交通大学学报, 2026, 60(2): 246-255.
[3]	. 基于输入映射及事件触发自适应策略的刚柔混合机械臂模型预测控制[J]. J Shanghai Jiaotong Univ Sci, 2026, 31(1): 36-47.
[4]	陈亮汶, 朱宇昕, 沈涛, 俞羿帆, 凌霄, 盛庆红. 基于红外尾迹匹配的舰船目标检测算法[J]. 空天防御, 2026, 9(1): 80-90.
[5]	罗志军, 王健瑞, 殷佳伟. 复杂战场环境下的任务驱动智能目标识别方法综述[J]. 空天防御, 2026, 9(1): 1-11.
[6]	王语阳, 张琛, 张宇, 王一鸣, 许颇, 蔡旭. 提升弱网有功稳定输出能力的光伏逆变器Q-V下垂系数在线调整方法[J]. 上海交通大学学报, 2025, 59(6): 845-856.
[7]	荣光, 张业鑫, 唐朝, 陈金宝, 周奕玲, 王建园. 基于仿真数据驱动的无人飞行器故障诊断技术研究[J]. 空天防御, 2025, 8(6): 73-84.
[8]	高磊, 马骏超, 吕敬, 刘佳宁, 王晨旭, 蔡旭. 基于频域模态法的新能源电力系统振荡稳定性评估[J]. 上海交通大学学报, 2025, 59(6): 821-835.
[9]	. 浮动式多机器人协调吊运系统负载稳定性分析[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1162-1170.
[10]	. 基于深度学习序列方法的多人姿态估计用来检测人体与关键点位置[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1103-1113.
[11]	. 基于三维卷积特征金字塔网络的高光谱卫星图像分类[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(6): 1073-1084.
[12]	夏伊琳, 刘刚, 鄢丛强, 蔡云泽. 基于深度学习的SAR图像舰船尾迹旋转框检测算法研究[J]. 空天防御, 2025, 8(5): 64-74.
[13]	许强, 马跃华, 许可, 潘俊. 雷达目标智能识别方法研究综述[J]. 空天防御, 2025, 8(5): 1-9.
[14]	. 基于CEEMDAN 和 GRU的停车位预测[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 962-975.
[15]	. 基于ALBERT的中国诗酒文化命名实体识别[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(5): 1065-1072.