Detail publikace
Utilization of Machine Learning in Vibrodiagnostics
ZUTH, D. MARADA, T.
Anglický název
Utilization of Machine Learning in Vibrodiagnostics
Typ
článek v časopise ve Scopus, Jsc
Jazyk
en
Originální abstrakt
The article deals with possibilities of use machine learning in vibrodiagnostics to determine a fault type of the rotary machine. Sample data are simulated according to the expected vibration velocity waveform signal at a specific fault. Then the data are pre-processed and reduced for using Matlab Classification Learner which creates a model for identifying faults in the new data samples. The model is finally tested on a new sample data. The article serves to verify the possibility of this method for later use on a real machine. In this phase is tested data preprocessing and a suitable classification method.
Anglický abstrakt
The article deals with possibilities of use machine learning in vibrodiagnostics to determine a fault type of the rotary machine. Sample data are simulated according to the expected vibration velocity waveform signal at a specific fault. Then the data are pre-processed and reduced for using Matlab Classification Learner which creates a model for identifying faults in the new data samples. The model is finally tested on a new sample data. The article serves to verify the possibility of this method for later use on a real machine. In this phase is tested data preprocessing and a suitable classification method.
Klíčová slova anglicky
Classification learner, Classification method, Machine learning, Matlab, Neuron network, Parallel Misalignment, PCA, Static unbalance, SVN, Vibrodiagnostics
Vydáno
05.08.2018
Nakladatel
Springer Verlag
Místo
Cham
ISBN
978-3-319-97887-1
ISSN
2194-5357
Kniha
Recent Advances in Soft Computing
Číslo
2017
Strany od–do
271–278
Počet stran
8
BIBTEX
@article{BUT149453,
author="Daniel {Zuth} and Tomáš {Marada},
title="Utilization of Machine Learning in Vibrodiagnostics",
booktitle="Recent Advances in Soft Computing",
year="2018",
number="2017",
month="August",
pages="271--278",
publisher="Springer Verlag",
address="Cham",
isbn="978-3-319-97887-1",
issn="2194-5357"
}