Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10256075" target="_blank" >RIV/61989100:27230/25:10256075 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001366762100001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001366762100001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app142210464" target="_blank" >10.3390/app142210464</a>

Result language

angličtina

Original language name

Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage

Original language description

Featured Application: This work can potentially be applied to industrial belt conveyors of any type. The tested system can be used for real-time monitoring in order to identify and prevent overloads, misalignments, growing damage to the belt in the early stages, and other trends that may cause failure. This paper is devoted to the real-time monitoring of close transportation devices, namely, belt conveyors. It presents a novel measurement system based on the linear strain gauges placed on the tail pulley surface. These gauges enable the monitoring and continuous collection and processing of data related to the process. An initial assessment of the machine learning application to the load identification was made. Among the tested algorithms that utilized machine learning, some exhibited a classification accuracy as high as 100% when identifying the load placed on the moving belt. Similarly, identification of the preset damage was possible using machine learning algorithms, demonstrating the feasibility of the system for fault diagnosis and predictive maintenance.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20300 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Volume of the periodical

14

Issue of the periodical within the volume

22

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

nestránkováno

UT code for WoS article

001366762100001

EID of the result in the Scopus database

2-s2.0-85210225747