中文摘要：

提出一种利用主成分分析和联合概率密度判别器进行新煤种在线辨识的方法。根据不同煤质的煤的燃烧火焰在初燃区的特征不同,利用光电传感器获得燃烧火焰初燃区的光辐射信号,提取信号在时域和频域内的特征值,经过主成分分析处理得到正交化的、维数压缩的特征值向量。针对几种已知的煤种,利用获得的正交化特征值向量数据,建立每一煤种的联合概率密度分布模型。利用基于该模型的判别器,可以进行新煤种的判别或已知燃煤种类的辨识。

英文摘要：

This paper presents a novel approach for on-line identification of new fuel type,which combines the principal component analysis technique and joint probability density arbiter.Because combustion flames of different coals have different oscillating features at the root area of the flame,a flame detector is utilized to capture the flame oscillation signals.Then the flame features are extracted in time and frequency domains from each flame oscillation signal,which form original feature vectors.The principal component analysis technique is utilized to transform each original feature vector into an orthogonal and dimension-reduced feature vector.Aiming at several known fuel types,a joint probability model is established for each fuel type using the data of the orthogonal feature vector.Then the joint probability density arbiters based on the models are used to determine whether the type of the fuel is new and identify the type of the fuel being burnt if it is one of the known fuel types.