Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F15%3A00082089" target="_blank" >RIV/00216224:14310/15:00082089 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60162694:G43__/15:00520703

Výsledek na webu

<a href="http://dx.doi.org/10.1177/1748006X14547789" target="_blank" >http://dx.doi.org/10.1177/1748006X14547789</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/1748006X14547789" target="_blank" >10.1177/1748006X14547789</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data

Popis výsledku v původním jazyce

At present, numerous approaches are devoted to monitoring a system state. Their intention is to determine the current state of a system and predict reliability parameters for the future. This article addresses one of the several possible approaches thatallows us to determine a system technical state on the basis of diagnostic data. These diagnostic data are from the area of tribiodagnostics, namely, engine oil. The article examines iron and lead particles that are selected deliberately with respect totheir origin in kinematic parts of the system and their degree of correlation with operation measures. The particles occur in oil during both operating time and calendar time development. To model their occurrence during operation time, we have used, inthe first part of the article, a mathematical regression method to set parameters. In the second part, we have applied a diffusion model based on a Wiener process. The results confirm that we are able to estimate the residual technical li

Název v anglickém jazyce

Contribution to system failure occurrence prediction and to system remaining useful life estimation based on oil field data

Popis výsledku anglicky

At present, numerous approaches are devoted to monitoring a system state. Their intention is to determine the current state of a system and predict reliability parameters for the future. This article addresses one of the several possible approaches thatallows us to determine a system technical state on the basis of diagnostic data. These diagnostic data are from the area of tribiodagnostics, namely, engine oil. The article examines iron and lead particles that are selected deliberately with respect totheir origin in kinematic parts of the system and their degree of correlation with operation measures. The particles occur in oil during both operating time and calendar time development. To model their occurrence during operation time, we have used, inthe first part of the article, a mathematical regression method to set parameters. In the second part, we have applied a diffusion model based on a Wiener process. The results confirm that we are able to estimate the residual technical li

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

ISSN

1748-006X

e-ISSN

—

Svazek periodika

229

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

36-45

Kód UT WoS článku

000349221400004

EID výsledku v databázi Scopus

—