A high accuracy method for the sintering condition recognition of rotary kiln

Abstract

According to the different material sintering conditions, the sintering conditions of alumina rotary kiln can be divided into: super-heated, super-chilled, and normal. In this paper, based on Local Binary Pattern (LBP) and the primary-color method, a novel feature extraction method is proposed to obtain information about temperature in flame images without calibrating camera parameters. Through the analysis of the problem as well as the experimental phenomena, a new classification procedure is devised: in the first step, the super-chilled condition is first separated, in the second step, the normal and super-heat condition are classified. Different feature extraction methods are used in the two steps mentioned above. One is to extract the texture features of pseudo temperature images, and short-time energy is used to describe the dynamic features. The other is to extract the texture features of grey-images by our improved LBPP,Rriu2, and characterize the dynamics with sample entropy and variance. Finally, experimental results show that the our feature extraction method can effectively reduce intra-class variation, increase inter-class variation and receive a high classification accuracy.