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ZUTH, D. MARADA, T.
Original Title
Using artificial intelligence to determine the type of rotary machine fault
Type
journal article in Scopus
Language
English
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
Classification learner, Classification method, Dynamic unbalance, Industry 4.0, Machine learning, Matlab, Neuron network, Static unbalance, Vibrodiagnostics
Authors
ZUTH, D.; MARADA, T.
Released
21. 12. 2018
Publisher
Brno University of Technology
Location
Brno, Czech Republic
ISBN
1803-3814
Periodical
Mendel Journal series
Year of study
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" }