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The influence of the evolution rule of basicity (0.6∼2.4) on the mineral composition and microstructure of sinter is studied by using a polarizing microscope, and the comprehensive application analysis of the drum index, vertical sintering speed, and yield of sinter shows that, over the course of an increase in basicity (0.6∼1.0), the mineral structure changed from the original porphyritic-granular structure to a porphyritic structure. At the same time, there was no calcium ferrite phase in the bonding phase at a basicity of less than 1.0; therefore, the downward trend of the three indicators is obvious. When the basicity was further increased to approximately 1.6, the main structure of the mineral phase changed from a corrosion structure to an interweaving corrosion structure. Because of the existence of a porphyritic-granular structure, the structure of the mineral phase was extremely inhomogeneous and most complex near the basicity of 1.6; although a small amount of calcium ferrite displayed an acicular structure, the drum index appeared to show a very low value. With an increase in basicity to 2.0, the mineral phase structure was dominated by an interweaving corrosion structure with a uniform framework, and the content of calcium ferrite reached the highest value. Moreover, a clear acicular structure developed, and the drum index also increased to the highest value. At a basicity of more than 2.0, a mineral structure began to appear and the corrosion, porphyritic-granular structure, and the drum index also showed a slightly declining trend. Therefore, in the actual production process, basicity should be avoided as far as possible at around 1.0 and 1.6 and it should be controlled at around 2.0. At the same time, based on the mineral facies data set of this paper, the convolutional neural network is used to carry out a simple prediction model experiment on the basicity corresponding to the mineral facies photos, and the effect is quite good, which provides a new idea and method for the follow-up study of mineral facies., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Jian-Ming Zhi et al.)

It is a big challenge to realize accurate security detection of blast furnace bearing at the same time so as to guarantee the security of equipment. To end this problem, this paper proposed a computer vision technology based on sensor data and hybrid deep learning method for the solution. We use Variational Mode Decomposition (VMD) algorithm which is a new time-frequency analysis method, which can decompose multi-component signals into multiple single-component amplitude-modulated signals at one time to decompose and deal with the sensor data of bearing fault, so as to realize the effective stripping of fault components and original components from sensor data. Using the artificial intelligence mentioned above, the features can be quickly and accurately extracted. By combining the advantages of deep learning, we improve the coupling mechanism and implement a hybrid deep learning-based computer vision method which greatly improves the calculation speed and accuracy of bearing fault diagnosis. It can be fully connected with the feature extraction algorithm VMD, which overcomes the problem that the bearing feature component is easy to be submerged and difficult to extract under the condition of high temperature and strong noise. The results show that the optimal selection of parameters of computer vision technology based on sensor data and hybrid deep learning can be realized through training the sensor data obtained from the experiment. The optimized hybrid deep learning-based computer vision algorithm can achieve 97.4% bearing fault diagnosis hit rate, which is an advanced application of deep learning algorithm in the engineering field. [ABSTRACT FROM AUTHOR]