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"
}