System condition assessment based on mathematical analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F18%3A00536304" target="_blank" >RIV/60162694:G43__/18:00536304 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26210/19:PU131217

Výsledek na webu

<a href="http://dx.doi.org/10.1109/IEEM.2018.8607623" target="_blank" >http://dx.doi.org/10.1109/IEEM.2018.8607623</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IEEM.2018.8607623" target="_blank" >10.1109/IEEM.2018.8607623</a>

Jazyk výsledku

angličtina

Název v původním jazyce

System condition assessment based on mathematical analysis

Popis výsledku v původním jazyce

When determining a system technical condition, it is possible to use multiple approaches. For practical reasons it is convenient to use an indirect diagnostic signal. In our article we focus on applying oil field data collected from a few tens of heavy vehicle engines. The aim is to get a picture of how quickly oil polluting particles are made and consequently how quickly the degradation progresses. This leads to system condition monitoring. When modelling the occurrence of the oil polluting particles, advanced linear regression methods are used. When analysing the diagnostic data, we use mainly a novel quantile regression approach. The aim is to estimate i) the course of trend in the development of polluting particles, ii) critical threshold time hitting iii) distribution of first hitting time of occurrence of soft failure.

Název v anglickém jazyce

System condition assessment based on mathematical analysis

Popis výsledku anglicky

When determining a system technical condition, it is possible to use multiple approaches. For practical reasons it is convenient to use an indirect diagnostic signal. In our article we focus on applying oil field data collected from a few tens of heavy vehicle engines. The aim is to get a picture of how quickly oil polluting particles are made and consequently how quickly the degradation progresses. This leads to system condition monitoring. When modelling the occurrence of the oil polluting particles, advanced linear regression methods are used. When analysing the diagnostic data, we use mainly a novel quantile regression approach. The aim is to estimate i) the course of trend in the development of polluting particles, ii) critical threshold time hitting iii) distribution of first hitting time of occurrence of soft failure.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

<a href="/cs/project/LO1202" target="_blank" >LO1202: NETME CENTRE PLUS</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2018 IEEE International Conference on Industrial Engineering and Engineering Management

ISBN

978-1-5386-6786-6

ISSN

2157-3611

e-ISSN

2157-362X

Počet stran výsledku

5

Strana od-do

222-226

Název nakladatele

IEEE

Místo vydání

Singapore

Místo konání akce

Bangkok

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000458674600046