Detail publikace

Utilization of Machine Learning in Vibrodiagnostics

ZUTH, D. MARADA, T.

Originální název

Utilization of Machine Learning in Vibrodiagnostics

Anglický název

Utilization of Machine Learning in Vibrodiagnostics

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.

Dokumenty

BibTex


@article{BUT149453,
  author="Daniel {Zuth} and Tomáš {Marada}",
  title="Utilization of Machine Learning in Vibrodiagnostics",
  annote="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.",
  address="Springer Verlag",
  booktitle="Recent Advances in Soft Computing",
  chapter="149453",
  doi="10.1007/978-3-319-97888-8_24",
  howpublished="print",
  institution="Springer Verlag",
  number="2017",
  year="2018",
  month="august",
  pages="271--278",
  publisher="Springer Verlag",
  type="journal article in Scopus"
}