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.

In order to study in depth the differences in basic characteristics between iron ore fines commonly used by a steel company, and guide the sintering performance plant to choose the best ore allocation method, experimental studies on the basic characteristics of seven iron ore powders of three sizes were carried out using micro-sintering equipment, mainly including assimilation properties, liquid phase fluidity, and bonding phase strength. The results of the research showed that with the increase of the iron ore powder particle size, the assimilation of the seven iron ore powders showed an overall decreasing trend, deteriorating fluidity and decreasing bonding phase strength. Among them, the overall fluidity of iron ore powder A was poor, and the fluidity of iron ore powder B varied greatly between different particle grades, and the fluidity of iron ore powder C was more balanced and its bonding phase strength was high, while the overall bonding phase strength of iron ore powders B and E was low. The results of the study provide a theoretical basis for optimal ore allocation in sintering plants.

In order to improve the quality of magnesia flux pellets and meet the production needs of the iron and steel industry, a pellet formation experiment was carried out. The effects of alkalinity R, SiO₂ mass fraction, MgO mass fraction on the green pellets’ burst temperature, compressive strength, and falling strength were studied. The results showed that with the increase in alkalinity, the bursting temperature of green pellets decreases, but has no obvious effect on the compressive strength or drop strength; with the increase in SiO₂ content, the bursting temperature of green pellets decreases gradually, and the green pellets’ strength also decreases slightly; with the increase in MgO content, the compressive strength of green pellets shows an upward trend, while the falling strength gradually decreases, and the burst temperature of green pellets shows a trend of rising first and then decreasing. The change trend is coupled with the software test data amplification method algorithm, based on the search algorithm of longicorn (MBAS), to expand a small amount of experimental data. Through data analysis and algorithm comparison, an improved generalized regression neural network (CFA-GRNN), based on culture firefly, was proposed to establish an optimization model for green pellet performance prediction. CFA uses the weights in the input layer and hidden layer of GRNN, the weights in the hidden layer and output layer, the threshold of the hidden layer and the threshold of the output layer as codes for optimization. The evolutionary goal is to obtain the most appropriate and optimal neural network structure. The results show that the MBAS algorithm, combined with the experimental research, can expand the effective data to 1000 pieces. Secondly, the green pellets’ burst temperature, compressive strength and falling strength predicted by the improved generalized regression neural network are in good agreement with the real values, and the average relative errors were 1.88%, 3.18% and 3.62%, respectively. The error analysis shows that the improved model algorithm has higher accuracy, meets the classification of pellets, and can be used to guide the production of pellets.