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Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network

Faculty of Electronics, Wroclaw University of Science and Technology, Janiszewskiego 11/17, 50-372 Wroclaw, Poland
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Academic Editor: Fabio La Foresta
Appl. Sci. 2021, 11(12), 5480; https://doi.org/10.3390/app11125480
Received: 18 May 2021 / Revised: 2 June 2021 / Accepted: 11 June 2021 / Published: 13 June 2021
(This article belongs to the Topic Industrial Engineering and Management)
Non-invasive conveyor belt diagnostics in damage detection allows significant reductions of the costs related to belt replacement, as well as the evaluation of belt usability and wear degree changes over time. As a result, it increases safety in the location where the belt is used. Depending on the location of a belt conveyor, its length or the type of the transported material, the belt may undergo wear at different rates, albeit the wear process itself is inevitable. This article presents an artificial intelligence-based approach to the classification of conveyor belt damage. A two-layer neural network was implemented in the MATLAB programming language, with the use of a Deep Learning Toolbox set. As a result of the optimization of the created network, the effectiveness of operation was at the level of 80%. View Full-Text
Keywords: neural networks; conveyor belts; magnetic method; diagnostics; automatic detection neural networks; conveyor belts; magnetic method; diagnostics; automatic detection
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MDPI and ACS Style

Kirjanów-Błażej, A.; Rzeszowska, A. Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network. Appl. Sci. 2021, 11, 5480. https://doi.org/10.3390/app11125480

AMA Style

Kirjanów-Błażej A, Rzeszowska A. Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network. Applied Sciences. 2021; 11(12):5480. https://doi.org/10.3390/app11125480

Chicago/Turabian Style

Kirjanów-Błażej, Agata, and Aleksandra Rzeszowska. 2021. "Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network" Applied Sciences 11, no. 12: 5480. https://doi.org/10.3390/app11125480

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