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火焰燃烧的特征量提取及稳定性识别
引用本文:黄耀松,刘石. 火焰燃烧的特征量提取及稳定性识别[J]. 现代电力, 2012, 29(3): 78-82
作者姓名:黄耀松  刘石
作者单位:华北电力大学能源动力与机械工程学院,北京,102206
基金项目:国家自然科学基金(50736002;61072005)
摘    要:为了有效提取火焰燃烧特征量,进而对火焰燃烧稳定性进行分析,文章通过理论分析和实验总结,首先提取了火焰强度方差及闪烁频率作为特征量,之后重点运用高阶统计量对火焰信号进行分析,提取了对火焰稳定性有较大影响的相位作为另一个新的特征量。最后考虑到各个特征量之间的相关性及量纲的影响,运用马氏距离作为判别准则,对火焰燃烧稳定性进行了有效的识别。结果表明,提取的3个特征量对火焰燃烧稳定性都有重要影响,而且运用马氏距离能够综合这3个特征量,使得从总体样本中选出的训练样本识别率达到93%,识别效果理想。

关 键 词:高阶统计量  马氏距离  火焰强度方差  闪烁频率  相位

Feature Extraction and Stability Recognition of Flame Combustion
HUANG Yaosong , LIU Shi. Feature Extraction and Stability Recognition of Flame Combustion[J]. Modern Electric Power, 2012, 29(3): 78-82
Authors:HUANG Yaosong    LIU Shi
Affiliation:(School of Energy,Power and Mechanical Engineering,North China Electric Power University,Beijing 102206,China)
Abstract:To extract the features of flame combustion and recognize the flame stability effectively,theoretical analysis and experimental summary were taken.Firstly,the paper extracted intensity variance and flicker frequency of flame as a feature.Then,appling higher-order statistics analysis to the flame signal,the phase that had greater impact on flame stability was extracted as another one.Finally,we used the Mahalanobis distance as a criterion to recognize the flame stability effectively.Results show that the features extracted have great impact on flame stability,and the Mahalanobis distance can synthesize these features,therefore make the recognition rate of the training samples chosen from the total samples reach to 93%.
Keywords:higher-order statistics  Mahalanobis distance  flame intensity variance  flicker frequency  phase
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