计量学报2025，Vol.46Issue(11)：1568-1573,6.DOI:10.3969/j.issn.1000-1158.2025.11.03
基于支持向量机的多负荷燃烧器火焰状态识别
Support Vector Machine Based Burner Flame States Identification under Multiple Loading Conditions
摘要
Abstract
The effective monitoring of burner flame status in coal-fired power plants is crucial to production safety,energy-saving and emission reduction.Existing fire detection systems mainly focus on detecting the presence of flame as the main task,and cannot make accurate judgments on the state of the flame.Therefore,a method for determining the flame states with different load conditions is proposed.The support vector machines are used as a basic classifier to combine the flame color and contour characteristics,which are derived from the flame images obtained in an actual power plant.Furthermore,an improved particle swarm optimization algorithm is employed for parameter optimization.Experimental results demonstrate that with sufficient data,the proposed method achieves a classification accuracy of more than 99% on flame images with three load conditions.In addition,the improved parameter optimization algorithm outperforms traditional methods,and the proposed method exhibits better performance with low dataset capacity.关键词
火焰状态监测/燃烧诊断/数字图像处理/支持向量机/粒子群优化算法Key words
flame states monitoring/combustion diagnosis/digital image processing/support vector machine/particle swarm optimization algorithm分类
通用工业技术引用本文复制引用
QIAN Xiangchen,MA Yun,XU Weicheng,FU Wei..基于支持向量机的多负荷燃烧器火焰状态识别[J].计量学报,2025,46(11):1568-1573,6.基金项目
国家自然科学基金(51827808) （51827808）
