Publication result detail

Using artificial intelligence to determine the type of rotary machine fault

ZUTH, D.; MARADA, T.

Original Title

Using artificial intelligence to determine the type of rotary machine fault

English Title

Using artificial intelligence to determine the type of rotary machine fault

Type

Scopus Article

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

ZUTH, D.; MARADA, T.

RIV year

2020

Released

21.12.2018

Publisher

Brno University of Technology

Location

Brno, Czech Republic

ISBN

1803-3814

Periodical

Mendel Journal series

Volume

24

Number

2

State

Czech Republic

Pages from

49

Pages to

54

Pages count

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"
}