Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10252185" target="_blank" >RIV/61989100:27240/23:10252185 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824704/" target="_blank" >https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824704/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s23010477" target="_blank" >10.3390/s23010477</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service
Popis výsledku v původním jazyce
This article deals with a unique, new powertrain diagnostics platform at the level of a large number of EU25 inspection stations. Implemented method uses emission measurement data and additional data from significant sample of vehicles. An original technique using machine learning that uses 9 static testing points (defined by constant engine load and constant engine speed), volume of engine combustion chamber, EURO emission standard category, engine condition state coefficient and actual mileage is applied. An example for dysfunction detection using exhaust emission analyses is described in detail. The test setup is also described, along with the procedure for data collection using a Mindsphere cloud data processing platform. Mindsphere is a core of the new Platform as a Service (Paas) for data processing from multiple testing facilities. An evaluation on a fleet level which used quantile regression method is implemented. In this phase of the research, real data was used, as well as data defined on the basis of knowledge of the manifestation of internal combustion engine defects. As a result of the application of the platform and the evaluation method, it is possible to classify combustion engine dysfunctions. These are defects that cannot be detected by self-diagnostic procedures for cars up to the EURO 6 level.
Název v anglickém jazyce
Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service
Popis výsledku anglicky
This article deals with a unique, new powertrain diagnostics platform at the level of a large number of EU25 inspection stations. Implemented method uses emission measurement data and additional data from significant sample of vehicles. An original technique using machine learning that uses 9 static testing points (defined by constant engine load and constant engine speed), volume of engine combustion chamber, EURO emission standard category, engine condition state coefficient and actual mileage is applied. An example for dysfunction detection using exhaust emission analyses is described in detail. The test setup is also described, along with the procedure for data collection using a Mindsphere cloud data processing platform. Mindsphere is a core of the new Platform as a Service (Paas) for data processing from multiple testing facilities. An evaluation on a fleet level which used quantile regression method is implemented. In this phase of the research, real data was used, as well as data defined on the basis of knowledge of the manifestation of internal combustion engine defects. As a result of the application of the platform and the evaluation method, it is possible to classify combustion engine dysfunctions. These are defects that cannot be detected by self-diagnostic procedures for cars up to the EURO 6 level.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
<a href="/cs/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: Platforma pro výzkum orientovaný na Průmysl 4.0 a robotiku v ostravské aglomeraci</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Sensors
ISSN
1424-3210
e-ISSN
1424-8220
Svazek periodika
23
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
18
Strana od-do
—
Kód UT WoS článku
000910259500001
EID výsledku v databázi Scopus
—