城市固废焚烧过程燃烧线极端异常火焰图像对抗生成
Combustion Line Extreme Abnormal Flame Image Adversarial Generation for Municipal Solid Waste Incineration Processes
郭海涛 1汤健 1夏恒 1乔俊飞2
作者信息
- 1. 北京工业大学信息学部,北京市 朝阳区 100024
- 2. 智慧环保北京实验室,北京市 朝阳区 100124
- 折叠
摘要
燃烧线是表征城市固废焚烧(municipal solid waste incineration,MSWI)过程燃烧稳定性的关键参数之一.完备的火焰图像模板库是实现燃烧线量化以从检测视角代替依靠运行专家"人工看火",进而通过实时反馈提升MSWI过程控制的智能化水平的基础.针对燃烧线极端异常火焰图像缺失问题,该文提出基于机理知识和对抗网络的燃烧线极端异常火焰图像生成方法.首先,基于焚烧炉内三维空间位置到图像像素点的机理映射关系分析燃烧线极端异常火焰图像,通过对正常火焰图像像素点的平移、拼接和组合等方式获取伪标记的燃烧线极端异常火焰图像;然后,采用循环生成 对 抗 网 络(cycle generative adversarial networks,CycleGAN)获得符合真实火焰图像分布的候选图像;最后,提出组合基于弗雷歇距离(Fréchet inception distance,FID)评估最优模型参数和根据伪标记筛选最终燃烧线极端异常火焰图像的 2 级评估与筛选策略.针对北京某MSWI厂的实验结果表明:依据燃烧线可划分图像为51%～73.6%正常、47%～51%和 73.6%～100%异常、0%～47%极端异常;当第 2级评估阈值设定为0.4时,所提方法生成合格极端异常火焰图像的比例为85.7%,优于传统评估方法.
Abstract
Combustion line is one of the key parameters to represent the combustion stability of municipal solid waste incineration(MSWI)processes.A complete flame image template library is the foundation for achieving combustion line quantification from a detection perspective instead of relying on operational experts to manually observe the flame,and improving the intelligence level of MSWI process control through real-time feedback.Aiming at the problem of missing combustion line flame image under extreme combustion state,a generation method based on fusion of mechanism knowledge and adversarial technology is proposed.First,based on the mechanism mapping relationship between the three-dimensional spatial position in the incinerator and the image pixels,the extreme abnormal flame samples are analyzed.The pseudo-label samples are obtained by shifting,stitching and combining the pixel points of normal sample.Then,the candidate extreme abnormal flames are obtained by using cycle generative adversarial networks(CycleGAN).Finally,a new two-level evaluation strategy is designed by combing the optimal model parameters evaluation based on Fréchet Inception Distance(FID)and the final extreme abnormal flame image selection according to the pseudo label.The experimental results based on a MSWI plant of Beijing show that the combustion line can be categorized into three ranges:51%to 73.6%as normal,47%to 51%and 73.6%to 100%as abnormal,and 0%to 47%as extreme abnormal..When the second level evaluation threshold is set to 0.4,the proportion of the satisfied extreme abnormal flame image generated by the proposed method is 85.7%,which is better than the traditional evaluation method.
关键词
城市固废焚烧/燃烧线量化/燃烧线极端异常火焰图像/循环生成对抗网络/图像评估与选择Key words
municipal solid waste incineration(MSWI)/quantification of combustion line/extreme abnormal flame image/cycle generative adversarial networks(CycleGAN)/image evaluation and selection引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据