Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

Original language description

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>

Czech name

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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

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OECD FORD branch

20301 - Mechanical engineering

Project

<a href="/en/project/EF16_026%2F0008404" target="_blank" >EF16_026/0008404: Machine Tools and Precision Engineering</a><br>

Continuities

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

Publication year

2021

Confidentiality

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

Name of the periodical

Machines

ISSN

2075-1702

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

1-19

UT code for WoS article

000712628700001

EID of the result in the Scopus database

2-s2.0-85116610879