Rotating machinery fault diagnosis using a quadratic neural unit

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F22%3A43896296" target="_blank" >RIV/44555601:13440/22:43896296 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21220/22:00361188
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2542660521000391" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2542660521000391</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1504/IJGUC.2022.124403" target="_blank" >10.1504/IJGUC.2022.124403</a>

Result language
angličtina
Original language name
Rotating machinery fault diagnosis using a quadratic neural unit
Original language description
In this work, a quadratic neural unit was implemented for rotating machinery fault diagnoses of an industrial machine, where the input data that were used were taken from a vibration test on an alternating current motor. The data that were obtained from the vibrometer were the frequency and the average of the vibration, which were previously trained and input into the neural unit. The output of this unit was a value that can be used to categorize the severity level of an engine, according to the severity table provided by the norm ISO 10816 for industrial machines.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)