Detail publikačního výsledku

Using artificial intelligence to determine the type of rotary machine fault

ZUTH, D.; MARADA, T.

Originální název

Using artificial intelligence to determine the type of rotary machine fault

Anglický název

Using artificial intelligence to determine the type of rotary machine fault

Druh

Článek Scopus

Originální abstrakt

The article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source is real measured data from the vibrodiagnostic model. This model allows simulation of some types of faults. The data is then processed and reduced for the use of the Matlab Classification learner app, which creates a model for recognizing faults. The model is ultimately tested on new samples of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in the time domain.

Anglický abstrakt

The article deals with the possibility of using machine learning in vibrodiagnostics to determine the type of fault of rotating machine. The data source is real measured data from the vibrodiagnostic model. This model allows simulation of some types of faults. The data is then processed and reduced for the use of the Matlab Classification learner app, which creates a model for recognizing faults. The model is ultimately tested on new samples of data. The aim of the article is to verify the ability to recognize similarly rotary machine faults from real measurements in the time domain.

Klíčová slova

Classification learner, Classification method, Dynamic unbalance, Industry 4.0, Machine learning, Matlab, Neuron network, Static unbalance, Vibrodiagnostics

Klíčová slova v angličtině

Classification learner, Classification method, Dynamic unbalance, Industry 4.0, Machine learning, Matlab, Neuron network, Static unbalance, Vibrodiagnostics

Autoři

ZUTH, D.; MARADA, T.

Rok RIV

2020

Vydáno

21.12.2018

Nakladatel

Brno University of Technology

Místo

Brno, Czech Republic

ISSN

1803-3814

Periodikum

Mendel Journal series

Svazek

24

Číslo

2

Stát

Česká republika

Strany od

49

Strany do

54

Strany počet

6

URL

https://mendel-journal.org/index.php/mendel/article/view/10

BibTex

@article{BUT159887,
  author="Daniel {Zuth} and Tomáš {Marada}",
  title="Using artificial intelligence to determine the type of rotary machine fault",
  journal="Mendel Journal series",
  year="2018",
  volume="24",
  number="2",
  pages="49--54",
  doi="10.13164/2018.2.049",
  issn="1803-3814",
  url="https://mendel-journal.org/index.php/mendel/article/view/10"
}