Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder

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...

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Main Authors: Piotr Bortnowski, Horst Gondek, Robert Król, Daniela Marasova, Maksymilian Ozdoba
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/16/4/1666
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author Piotr Bortnowski
Horst Gondek
Robert Król
Daniela Marasova
Maksymilian Ozdoba
author_facet Piotr Bortnowski
Horst Gondek
Robert Król
Daniela Marasova
Maksymilian Ozdoba
author_sort Piotr Bortnowski
collection DOAJ
description 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.
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spelling doaj.art-2c1cc8f0edd04f25b6176a67999c66a12023-11-16T20:16:06ZengMDPI AGEnergies1996-10732023-02-01164166610.3390/en16041666Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN AutoencoderPiotr Bortnowski0Horst Gondek1Robert Król2Daniela Marasova3Maksymilian Ozdoba4Department of Mining, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, Na Grobli 15, 50-421 Wrocław, PolandVSB—Department of Machine and Industrial Design, Technical University of Ostrava, 17 Listopadu 2172/15, 708 00 Ostrava, Czech RepublicDepartment of Mining, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, Na Grobli 15, 50-421 Wrocław, PolandInstitute of Logistics and Transport, Faculty BERG, Technical University of Košice, Park Komenského 14, 043 84 Košice, SlovakiaDepartment of Mining, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, Na Grobli 15, 50-421 Wrocław, PolandIn 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.https://www.mdpi.com/1996-1073/16/4/1666belt conveyortransfer pointchute monitoringanomaly detectionimage processingblockages state
spellingShingle Piotr Bortnowski
Horst Gondek
Robert Król
Daniela Marasova
Maksymilian Ozdoba
Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder
Energies
belt conveyor
transfer point
chute monitoring
anomaly detection
image processing
blockages state
title Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder
title_full Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder
title_fullStr Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder
title_full_unstemmed Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder
title_short Detection of Blockages of the Belt Conveyor Transfer Point Using an RGB Camera and CNN Autoencoder
title_sort detection of blockages of the belt conveyor transfer point using an rgb camera and cnn autoencoder
topic belt conveyor
transfer point
chute monitoring
anomaly detection
image processing
blockages state
url https://www.mdpi.com/1996-1073/16/4/1666
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AT horstgondek detectionofblockagesofthebeltconveyortransferpointusinganrgbcameraandcnnautoencoder
AT robertkrol detectionofblockagesofthebeltconveyortransferpointusinganrgbcameraandcnnautoencoder
AT danielamarasova detectionofblockagesofthebeltconveyortransferpointusinganrgbcameraandcnnautoencoder
AT maksymilianozdoba detectionofblockagesofthebeltconveyortransferpointusinganrgbcameraandcnnautoencoder