A vibration sensing method for longitudinal damage of conveyor belt based on infrared computer vision
A technology of computer vision and conveyor belts, which is applied to computer parts, calculations, neural learning methods, etc., and can solve problems such as semi-contact, easy interference of mathematical models, and poor practicability
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[0029] like Figure 1 to Figure 7 As shown, a method for perceiving longitudinal damage and vibration of a conveyor belt based on infrared computer vision of the present invention includes the following steps:
[0030] Step 1: Build an image data set: Set a high-speed camera above the mining conveyor belt to collect the micro-vibration images of the conveyor belt in normal, worn, scratched, and torn states, and store them on the tower server. The dataset is used to initially train the convolutional neural network, and the other part of the dataset is used to further train the convolutional neural network;
[0031] Step 2: Use the convolutional neural network with variable convolution kernel to train and test the vibration frequency signals of the mining conveyor belt in the normal state, wear state, scratch state and tear state respectively, and obtain the initially trained convolutional neural network. network;
[0032] Step 3: Apply the initially trained convolutional neur...
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