Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41110%2F21%3A88589" target="_blank" >RIV/60460709:41110/21:88589 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2079-9292/10/24/3145" target="_blank" >https://www.mdpi.com/2079-9292/10/24/3145</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/electronics10243145" target="_blank" >10.3390/electronics10243145</a>
Alternative languages
Result language
angličtina
Original language name
Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods
Original language description
Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect clogging of drainage channels showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50202 - Applied Economics, Econometrics
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Electronics (Schweiz)
ISSN
2079-9292
e-ISSN
2079-9292
Volume of the periodical
10
Issue of the periodical within the volume
24
Country of publishing house
CH - SWITZERLAND
Number of pages
18
Pages from-to
1-18
UT code for WoS article
000737806000001
EID of the result in the Scopus database
2-s2.0-85121302627