Detection of blockages of the belt conveyor transfer point using an RGB camera and CNN autoencoder

Abstract

In the material transfer area, the belt is exposed to considerable damage, the energy of falling material is lost, and there is significant dust and noise. One of the most common causes of failure is transfer chute blockage, when the flow of material in the free fall or loading zone is disturbed by oversized rock parts or other objects, e.g., rock bolts. The failure of a single transfer point may cause the entire transport route to be excluded from work and associated with costly breakdowns. For this reason, those places require continuous monitoring and special surveillance measures. The number of methods for monitoring this type of blockage is limited. The article presents the research results on the possibility of visual monitoring of the transfer operating status on an object in an underground copper ore mine. A standard industrial RGB camera was used to obtain the video material from the transfer point area, and the recorded frames were processed by a detection algorithm based on a neural network. The CNN autoencoder was taught to reconstruct the image of regular transfer operating conditions. A data set with the recorded transfer blockage state was used for validation.