Using Artificial Intelligence to Determine the Type of Rotary Machine Fault

Thumbnail Image
Files
10-Article Text-10-1-10-20190218.pdf(585.8 KB)
Date
2018-12-21
Authors
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Automation and Computer Science, Brno University of Technology
Altmetrics
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 Classication 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.
Description
Keywords
Vibrodiagnostics, Neuron Network, Classification Learner, Machine Learning, Matlab, Industry 4.0, Classification Method, Static Unbalance, Dynamic Unbalance
Citation
Mendel. 2018 vol. 24, č. 2, s. 49–54. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/10
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en
Study field
Comittee
Date of acceptance
Defence
Result of defence
Document licence

DOI
10.13164/mendel.2018.2.049
URI
http://hdl.handle.net/11012/179248
Collections
Vol. 24, No. 2
Citace PRO