Publication detail

Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

ZUTH, D. BLECHA, P. MARADA, T. HUZLÍK, R. TŮMA, J. MARADOVÁ, K. FRKAL, V.

Original Title

Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

vibrodiagnostics; classification learner app; machine learning; MATLAB; Python; classification model; unbalance

Authors

ZUTH, D.; BLECHA, P.; MARADA, T.; HUZLÍK, R.; TŮMA, J.; MARADOVÁ, K.; FRKAL, V.

Released

30. 9. 2021

Publisher

MDPI

Location

BASEL, Switzerland

ISBN

2075-1702

Periodical

Machines

Year of study

9

Number

10

State

Swiss Confederation

Pages from

1

Pages to

19

Pages count

19

URL

https://www.mdpi.com/2075-1702/9/10/222

Full text in the Digital Library

http://hdl.handle.net/11012/203230

BibTex

@article{BUT175274,
  author="Daniel {Zuth} and Petr {Blecha} and Tomáš {Marada} and Rostislav {Huzlík} and Jiří {Tůma} and Karla {Maradová} and Vojtěch {Frkal}",
  title="Vibrodiagnostics Faults Classification for the Safety Enhancement of Industrial Machinery",
  journal="Machines",
  year="2021",
  volume="9",
  number="10",
  pages="1--19",
  doi="10.3390/machines9100222",
  issn="2075-1702",
  url="https://www.mdpi.com/2075-1702/9/10/222"
}