Rotary kiln combustion working condition recognition based on flame image texture features and LVQ neural network

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process, a combustion working condition recognition method based on learning vector quantization (LVQ) neural network is introduced. Firstly, the numerical flame image was analyzed to extract texture features, such as energy, entropy and inertia, based on grey-level co-occurrence matrix (GLCM) to provide qualitative information on the changes in the visual appearance of the flame. Then kernel principal component analysis (KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the LVQ target dimension and network scale greatly. Finally, LVQ neural network is trained and recognized by using the normalized texture feature datum. Test results show that the proposed KPCA-LVQ classifier has an excellent performance on training speed and correct recognition ratio and meets the requirement for the real-time combustion working conditions recognition.