Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 6 May 2021 / Approved: 7 May 2021 / Online: 7 May 2021 (09:03:23 CEST)

A peer-reviewed article of this Preprint also exists.

Olchówka, D.; Rzeszowska, A.; Jurdziak, L.; Błażej, R. Statistical Analysis and Neural Network in Detecting Steel Cord Failures in Conveyor Belts. Energies 2021, 14, 3081. Olchówka, D.; Rzeszowska, A.; Jurdziak, L.; Błażej, R. Statistical Analysis and Neural Network in Detecting Steel Cord Failures in Conveyor Belts. Energies 2021, 14, 3081.

Journal reference: Energies 2021, 14, 3081
DOI: 10.3390/en14113081

Abstract

The 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 devices parameters settings were the base for statistical analysis. It 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 its appropriate identification of damage. In both methods were used the same data sets, which made it possible to compare methods.

Subject Areas

conveyor belts; magnetic method; diagnostics; NDT method; belt damage; statistical analysis; neural networks

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