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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20300 - Mechanical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Svazek periodika

14

Číslo periodika v rámci svazku

22

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

nestránkováno

Kód UT WoS článku

001366762100001

EID výsledku v databázi Scopus

2-s2.0-85210225747