Utilization of Machine Learning in Vibrodiagnostics

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F18%3APU128776" target="_blank" >RIV/00216305:26210/18:PU128776 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-97888-8_24" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-97888-8_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-97888-8_24" target="_blank" >10.1007/978-3-319-97888-8_24</a>

Result language
angličtina
Original language name
Utilization of Machine Learning in Vibrodiagnostics
Original language description
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.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
20301 - Mechanical engineering

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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