Publication detail

Utilization of Machine Learning in Vibrodiagnostics

ZUTH, D. MARADA, T.

English title

Utilization of Machine Learning in Vibrodiagnostics

Type

journal article in Scopus

Language

en

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.

English 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 in English

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

Released

05.08.2018

Publisher

Springer Verlag

Location

Cham

ISBN

978-3-319-97887-1

ISSN

2194-5357

Book

Recent Advances in Soft Computing

Number

2017

Pages from–to

271–278

Pages count

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"
}