Examination of Multivalent Diagnoses Developed by a Diagnostic Program with an Artificial Neural Network for Devices in the Electric Hybrid Power Supply System “House on Water”

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F75081431%3A_____%2F21%3A00002098" target="_blank" >RIV/75081431:_____/21:00002098 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/1996-1073/14/8/2153/htm" target="_blank" >https://www.mdpi.com/1996-1073/14/8/2153/htm</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Examination of Multivalent Diagnoses Developed by a Diagnostic Program with an Artificial Neural Network for Devices in the Electric Hybrid Power Supply System “House on Water”

Popis výsledku v původním jazyce

This article presents the problem of diagnostic examination by the (DIAG) diagnostic system of devices of the House on Water (HoW) hybrid electric power system in the multi-valued (2, 3, and 4) state assessment. Forming the basis for the functioning of the (DIAG) diagnostic system is the measurement knowledge base of the object tested. For this purpose, the issues of building a diagnostic knowledge base for a hybrid power system for HoW are presented. The basis for obtaining diagnostic information for the measurement knowledge base is a functional and diagnostic analysis of the hybrid power system tested. The result of this analysis is a functional and diagnostic model of the research object. At the next stage of the work, on the basis of the model created, the sets of basic elements and the sets of measurement signals were determined together with the reference signals assigned. State classification in the (DIAG) system is based on an analysis of the value of the divergence metrics of the signal vectors tested. The purpose of the HoW diagnostic test is to assess an increase in the diagnoses developed by the intelligent diagnostic system (DIAG 2) in 4-valued logic in relation to the assessments in 3- and 2-valued logic.

Název v anglickém jazyce

Examination of Multivalent Diagnoses Developed by a Diagnostic Program with an Artificial Neural Network for Devices in the Electric Hybrid Power Supply System “House on Water”

Popis výsledku anglicky

This article presents the problem of diagnostic examination by the (DIAG) diagnostic system of devices of the House on Water (HoW) hybrid electric power system in the multi-valued (2, 3, and 4) state assessment. Forming the basis for the functioning of the (DIAG) diagnostic system is the measurement knowledge base of the object tested. For this purpose, the issues of building a diagnostic knowledge base for a hybrid power system for HoW are presented. The basis for obtaining diagnostic information for the measurement knowledge base is a functional and diagnostic analysis of the hybrid power system tested. The result of this analysis is a functional and diagnostic model of the research object. At the next stage of the work, on the basis of the model created, the sets of basic elements and the sets of measurement signals were determined together with the reference signals assigned. State classification in the (DIAG) system is based on an analysis of the value of the divergence metrics of the signal vectors tested. The purpose of the HoW diagnostic test is to assess an increase in the diagnoses developed by the intelligent diagnostic system (DIAG 2) in 4-valued logic in relation to the assessments in 3- and 2-valued logic.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energies

ISSN

1996-1073

e-ISSN

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Svazek periodika

volume 14/

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

19

Strana od-do

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Kód UT WoS článku

000644144600001

EID výsledku v databázi Scopus

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