E3S Web Conf.
Volume 71, 2018XVIII Conference of PhD Students and Young Scientists “Interdisciplinary Topics in Mining and Geology”
|Number of page(s)||5|
|Published online||05 December 2018|
Regression method-based analysis of damage development in the core of steel cord conveyor belts
Wroclaw University of Science and Technology, Faculty of Geoengineering, Mining and Geology, 27 Wyb. Wyspiańskiego St., 50-370 Wroclaw, Poland
2 Wroclaw University of Science and Technology, Faculty of Electronics, Department of Systems and Computer Networks, 27 Wyb. Wyspiańskiego St., 50-370 Wroclaw, Poland
* Corresponding author: firstname.lastname@example.org
This paper is based on the data gathered with the use of the DiagBelt mobile system for non-invasive diagnostics of conveyor belts with steel-cord core. The object of the tests comprised a slow-burning ST 3150 conveyor belt, having width B-1200. The tests were performed on a belt conveyor operated in a Polish underground mine. Four tests were performed at different time intervals in order to monitor the development of conveyor belt core damage. The regression model was based on data gathered from several sections of the inspected belt and on three indicators of defects (the sum, the number and the surface of defects). The analysis was performed not only for the linear regression model, but also for the quadratic regression model, which seems to provide promising results.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.