Statistical Analysis and Neural Network in Detecting Steel Cord Failures in Conveyor Belts

Dominika Olchówka, Aleksandra Rzeszowska, Leszek Jurdziak and Ryszard Błażej
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Dominika Olchówka: Faculty of Geoengineering Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Aleksandra Rzeszowska: Faculty of Electronics, Wroclaw University of Science and Technology, Janiszewskiego 11/17, 50-372 Wroclaw, Poland
Leszek Jurdziak: Faculty of Geoengineering Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Ryszard Błażej: Faculty of Geoengineering Mining and Geology, Wroclaw University of Science and Technology, Na Grobli 15, 50-421 Wroclaw, Poland

Energies, 2021, vol. 14, issue 11, 1-11

Abstract: This paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images, obtained for numerous parameters settings of the device, were the base for statistical analysis. This analysis makes it possible to determine the Pearson’s linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach it appropriate identification of damage. In both methods, the same data sets were used, which made it possible to compare methods.

Keywords: conveyor belts; magnetic method; diagnostics; NDT method; belt damage; statistical analysis; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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