目的 为了进一步提高锅炉燃烧火焰图像状态识别的性能,提出了一种基于Log-Gabor小波和分数阶多项式核主成分分析(KPCA)的火焰图像状态识别方法。方法 首先利用Log-Gabor滤波器组对火焰图像进行滤波,提取滤波后图像的均值和标准差,并构成纹理特征向量。然后使用分数阶KPCA方法对纹理特征向量进行降维,并将降维后的纹理特征向量输入支持向量机进行分类。结果 本文与基于Log-Gabor小波特征提取以及2种基于Gabor小波特征提取的方法相比,本文方法的分类识别正确率更高,分类精度为76%。同时,第1主分量方差比重与核函数参数d之间满足递增关系。本文方法能够准确地提取火焰图像纹理特征。结论 本文提出一种对锅炉燃烧火焰图像进行状态识别的方法,对提取的火焰图像纹理特征向量进行降维并进行分类,可以获得较高的分类精度。实验结果表明,本文方法分类精度较高,运行时间较短,具有良好的实时性。

Objective To improve performance in the state identification of boiler combustion flame images, a state identification method based on the Log-Gabor wavelet and kernel principal component analysis (KPCA) with fractional power polynomial models is proposed. Method Flame images are filtered by the log-Gabor filter bank. The texture feature vectors of the images are constructed with the use of the mean and standard deviation of the filtered image. KPCA with fractional power polynomial models is utilized to reduce the dimension of the texture feature vectors. These dimension-reduced texture feature vectors are classified by a support vector machine. Result Experiment results show that the proposed method can accurately extract the texture features of the flame images. Compared with the feature extraction method based on the Log-Gabor wavelet and two other feature extraction methods based on the Gabor wavelet, the proposed method has a higher classification rate of 76%. The variance proportion of the first principal component increases as the kernel parameter d increases. Conclusion A state-identification method of boiler combustion flame images is proposed in this study. High classification accuracy can be achieved through a reduction in the dimension of the texture feature vectors of flame images. Experiment results show that the proposed method can obtain high classification accuracy. It also exhibits a short running time and good real-time performance.