Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU142510" target="_blank" >RIV/00216305:26210/21:PU142510 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2075-1702/9/10/222" target="_blank" >https://www.mdpi.com/2075-1702/9/10/222</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/machines9100222" target="_blank" >10.3390/machines9100222</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

Popis výsledku v původním jazyce

The current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination.</p>

Název v anglickém jazyce

Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

Popis výsledku anglicky

The current digitization of industrial processes is leading to the development of smart machines and smart applications in the field of engineering technologies. The basis is an advanced sensor system that monitors selected characteristic values of the machine. The obtained data need to be further analysed, correctly interpreted, and visualized by the machine operator. Thus the machine operator can gain a sixth sense for keeping the machine and the production process in a suitable condition. This has a positive effect on reducing the stress load on the operator in the production of expensive components and in monitoring the safe condition of the machine. The key element here is the use of a suitable classification model for data evaluation of the monitored machine parameters. The article deals with the comparison of the success rate of classification models from the MATLAB Classification Learner App. Classification models will compare data from the frequency and time domain, the data source is the same. Both data samples are from real measurements on the CNC vertical machining center (CNC-Computer Numerical Control). Three basic states representing machine tool damage are recognized. The data are then processed and reduced for the use of the MATLAB Classification Learner app, which creates a model for recognizing faults. The article aims to compare the success rate of classification models when the data source is a dataset in time or frequency domain and combination.</p>

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

<a href="/cs/project/EF16_026%2F0008404" target="_blank" >EF16_026/0008404: Strojírenská výrobní technika a přesné strojírenství</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Machines

ISSN

2075-1702

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

000712628700001

EID výsledku v databázi Scopus

2-s2.0-85116610879