中国电机工程学报2024，Vol.44Issue(11)：4376-4386,中插16,12.DOI:10.13334/j.0258-8013.pcsee.222452
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
摘要
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分类
信息技术与安全科学引用本文复制引用
郭海涛,汤健,夏恒,乔俊飞..城市固废焚烧过程燃烧线极端异常火焰图像对抗生成[J].中国电机工程学报,2024,44(11):4376-4386,中插16,12.基金项目
国家自然科学基金项目(62073006). Project Supported by National Natural Science Foundation of China(62073006). （62073006）
