Publication detail

Using artificial intelligence to determine the type of rotary machine fault

ZUTH, D. MARADA, T.

English title

Using artificial intelligence to determine the type of rotary machine fault

Type

Scopus Article

Language

en

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.

Keywords in English

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

Released

2018-12-21

Publisher

Brno University of Technology

Location

Brno, Czech Republic

ISSN

1803-3814

Volume

24

Number

2

Pages from–to

49–54

Pages count

6

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"
}