Computer Vision Technology Based on Sensor Data and Hybrid Deep Learning for Security Detection of Blast Furnace Bearing

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.