摘要
通过对火焰图像进行分析,提取火焰亮度、火焰面积、质心偏移距离和圆形度等7个特征量。然后基于主元分析技术,提出一种对燃烧火焰稳定性进行监视和诊断的方法,采用HotellingT2和Q两个统计量对每一时刻的图像数据向量进行监测,检验是否超过各自的控制限,只要这两个统计量之一越限,则可判定燃烧出现异常。实验结果表明:该方法能够在线实时地、有效地识别、判断火焰的燃烧状态,并且将结果以Q图、Hotelling T2图和主元图的形式直观地表示出来;该文同时应用支持向量机方法分别对特征向量和原始图像数据进行识别分类,结果表明基于主元分析原理和支持向量机方法所得到的结果是一致的。
Seven characteristic values ,such as flame luminance, flame area, centroid offset and etc. are extracted in analysing the flame image. And then based on principal component analysis (PCA), a method for monitoring and diagnosing stability of flame is put forward. Two statistics of Hotelling T2 and Q are used to monitor time-to-time image data vectors, and check them whether they exceed their own controllable limit. As long as any one of them exceeds the limit, abnormity of combustion should be concluded. An experimental research shows that the method helps in on-line and real-time recognizing and judging the combustion status of the burning flame, and gives a visual result with figures of Q, of Hotelling T2 and PCA; at the same time, the characteristic vector and the original image data identified and sorted by using a method of support vector machine(SVM), the results show that two method.one is based on PCA and another is by support vector machine, are quite accordant.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2004年第2期185-190,共6页
Proceedings of the CSEE
基金
国家自然科学基金项目(50106015)~~
关键词
锅炉
火焰监测
PCA
SVM
支持向量机
火焰图像检测系统
燃烧诊断
Combustion diagnosis
Principal component analysis
Flame image
Support Vector Machine(SVM)
Patterns distinction.
