Publication detail

Utilization of Machine Learning in Vibrodiagnostics

ZUTH, D. MARADA, T.

Original Title

Utilization of Machine Learning in Vibrodiagnostics

Type

journal article in Scopus

Language

English

Original Abstract

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.

Keywords

Classification learner, Classification method, Machine learning, Matlab, Neuron network, Parallel Misalignment, PCA, Static unbalance, SVN, Vibrodiagnostics

Authors

ZUTH, D.; MARADA, T.

Released

5. 8. 2018

Publisher

Springer Verlag

Location

Cham

ISBN

978-3-319-97887-1

Book

Recent Advances in Soft Computing

ISBN

2194-5357

Periodical

Advances in Intelligent Systems and Computing

Number

2017

State

Swiss Confederation

Pages from

271

Pages to

278

Pages count

8

URL

https://link.springer.com/chapter/10.1007%2F978-3-319-97888-8_24

BibTex

@article{BUT149453,
  author="Daniel {Zuth} and Tomáš {Marada}",
  title="Utilization of Machine Learning in Vibrodiagnostics",
  journal="Advances in Intelligent Systems and Computing",
  year="2018",
  number="2017",
  pages="271--278",
  doi="10.1007/978-3-319-97888-8\{_}24",
  issn="2194-5357",
  url="https://link.springer.com/chapter/10.1007%2F978-3-319-97888-8_24"
}