基于卷积自编码的火焰图像稳定性定量评估  

Quantitative Evaluation of Flame Image Stability Based on Convolutional Autoencoder

作　　者：王安 温武斌 刘宏文 韩哲哲 许传龙[4] WANG An;WEN Wubin;LIU Hongwen;HAN Zhezhe;XU Chuanlong(Guoneng Changzhou Power Generation Co.,Ltd.,Changzhou 213033,Jiangsu Province,China;CHN Energy Jiangsu Power Co.,Ltd.,Nanjing 215433,Jiangsu Province,China;School of Information and Communication Engineering,Nanjing Institute of Technology,Nanjing 211167,Jiangsu Province,China;School of Energy and Environment,Southeast University,Nanjing 210096,Jiangsu Province,China)

机构地区：[1]国能常州发电有限公司,江苏常州213033 [2]国家能源集团江苏电力有限公司,江苏南京215433 [3]南京工程学院信息与通信工程学院,江苏南京211167 [4]东南大学能源与环境学院,江苏南京210096

出　　处：《动力工程学报》2025年第3期325-333,共9页Journal of Chinese Society of Power Engineering

基　　金：国家重点研发计划资助项目(2023YFB4102904);国家自然科学基金资助项目(51976038);国家能源投资集团科技项目(GC-2023-075)。

摘　　要：提出了一种火焰图像稳定性定量评估方法,首先采用卷积自编码对火焰图像进行特征提取,然后利用定量评估指标予以特征分析。定量评估指标建立在图像特征的聚类分析和统计分析基础上,其数值区间为[0,1]。卷积自编码采用一种基于重建相似性的新损失函数,以提高训练效率。同时,在乙烯燃烧平台上开展试验研究,以验证火焰图像稳定性定量评估方法的有效性。结果表明:卷积自编码能够以无监督方式提取火焰图像特征,其性能明显优于传统特征学习方法;所建立的定量评估指标可以量化表征火焰图像稳定性,展现出极强的泛化能力。A quantitative evaluation method for flame image stability was proposed.First,convolutional autoencoder was used to extract features from flame images,and then quantitative evaluation index was employed for feature analysis.The quantitative evaluation index,with a numerical interval of[0,1],was established based on cluster analysis and statistical analysis of the image feature.The convolutional autoencoder adopted a novel loss function based on reconstruction similarity to improve training efficiency.The effectiveness of the quantitative evaluation method for flame image stability was verified through experiments on the ethylene combustion platform.Results show that the convolutional autoencoder can extract image features in an unsupervised manner,and its performance is obviously superior to traditional feature learning methods.In addition,the established quantitative evaluation index can quantitatively characterize the flame image stability,showing strong generalization ability.

关 键 词：火焰图像 图像特征 火焰脉动 卷积自编码 定量评估 

分 类 号：TK229.2[动力工程及工程热物理—动力机械及工程]