PCA和SVM在火焰监测中的应用研究  

A RESEARCH ON APPLICATION OF PCA AND SVM TO FLAME MONITORING

作　　者：白卫东[1] 严建华[1] 池涌[1] 王飞[1] 马增益[1] 林彬[1] 倪明江[1] 岑可法[1] 

机构地区：[1]浙江大学热能工程研究所能源清洁利用与环境环境教育部重点实验室,浙江杭州310027

出　　处：《中国电机工程学报》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)~~

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.

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 

分 类 号：TK227.1[动力工程及工程热物理—动力机械及工程]